robot_body_filter repository

Repository Summary

Checkout URI https://github.com/peci1/robot_body_filter.git
VCS Type git
VCS Version master
Last Updated 2021-07-30
Dev Status MAINTAINED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
robot_body_filter 1.2.0

README

robot_body_filter

Filters the robot's body out of point clouds and laser scans.

Changes vs PR2/robot_self_filter:

  • Now the package is a normal filters::FilterBase filter and not a standalone node.
  • Using both containment and ray-tracing tests.
  • Using all collision elements for each link instead of only the first one.
  • Enabling generic point type, removing PCL dependency and unnecessary params.
  • Using bodies.h and shapes.h from geometric_shapes.
  • As a by-product, the filter can compute robot's bounding box or sphere.

Build Status

Continuous integration

ROS Version Build Status
Melodic Build Status
Build Status

Dev job

ROS Version Build Status
Melodic Build Status
Noetic Build Status

Release jobs

ROS Version Ubuntu amd64 Ubuntu armhf Ubuntu arm64 Debian amd64 Debian arm64
Melodic Bionic Build Status Bionic Build Status Bionic Build Status Stretch Build Status Stretch Build Status
Noetic Focal Build Status Focal Build Status Focal Build Status Buster Build Status Buster Build Status

Basic Operation

filters::FilterBase API

The basic workings of this filter are done via the filters::FilterBase API implemented for sensor_msgs::LaserScan and sensor_msgs::PointCloud2 types. This means you can load this filter into a FilterChain along other filters as usual. Different from the standard filters, this one can also publish several interesting topics and subscribes to TF.

General overview

This filter reads robot model and the filter config, subscribes to TF, waits for data (laserscans or point clouds) and then cleans them from various artifacts (this is called data filtering).

It can perform 3 kinds of data filters: clip the data based on the provided sensor limits (parameter filter/do_clipping), remove points that are inside or on the surface of the robot body (parameter filter/do_contains_test) and remove points that are seen through a part of the robot body (parameter filter/do_shadow_test). These kinds of tests are further referenced as "clipping", "contains test" and "shadow test".

If working with point clouds, the filter automatically recognizes whether it works with organized or non-organized clouds. In organized clouds, it marks the filtered-out points as NaN. In non-organized clouds, it removes the filtered-out points. In laserscans, removal is not an option, so the filtered-out points are marked with NaN (some guides suggest that max_range + 1 should be used for marking invalid points, but this filter uses NaN as a safer option).

Performance tips

In general, the filter will be computationally expensive (clipping is fast, contains test is medium CPU intensive and shadow test is the most expensive part, because it basically performs raytracing). You can limit the required CPU power by limiting the filter only to parts that matter. E.g. if the robot has a link that can never be seen by the sensor, put it in the list of ignored links. The less links are processed, the better performance. If you're only interested in removing a few links, consider using the only_links parameter.

To speed up shadow filtering, you can set filter/max_shadow_distance, which limits the number of points considered for shadow tests just to points close to the sensor. Setting this to e.g. three times the diameter of the robot should remove all of the shadow points caused by refraction by a part of the robot body. But you have to test this with real data.

Performance also strongly depends on representation of the robot model. The filter reads <collision> tags from the robot URDF. You can use boxes, spheres and cylinders (which are fast to process), or you can use convex meshes (these are much worse performance-wise). If you pass a non-convex mesh, its convex hull will be used for the tests. Don't forget that each link can have multiple <collision> tags. If you do not have time to convert your meshes to the basic shapes (there is probably no good tool for it :(, just trial and error with restarting RViz), try to at least reduce the number of triangles in your meshes. You can use your high-quality meshes in <visual> tags.

Model inflation

You can utilize the builtin model inflation mechanism to slightly alter the size of the model. You will probably want to add a bit "margin" to the contains and shadow tests so that points that are millimeters outside the robot body will anyways get removed. You can set a default scale and padding which are used for all collisions. Different inflation can be used for contains tests and for shadow tests. Inflation can also be specified differently for each link. Look at the body_model/inflation/* parameters for details.

Scaling means multiplying the shape dimensions by the given factor (with its center staying in the same place). Padding means adding the specified metric distance to each "dimension" of the shape. Padding a sphere by p just adds p to its radius; padding a cylinder adds p to its radius and 2p to its length (you pad both the top and the bottom of the cylinder); padding a box adds 2p to all its extents (again, you pad both of the opposing sides); padding a mesh pads each vertex of its convex hull by p along the direction from the mesh center to the vertex (mesh center is the center specified in the mesh file, e.g. DAE). Have a good look at the effects of mesh padding, as the results can be non-intuitive.

Data acquisition modes

The filter supports data captured in two modes - all at once (e.g. RGBD cameras), or each point at a different time instant (mostly lidars). This is handled by sensor/point_by_point setting. Each mode supports both laser scans and point cloud input (although all-at-once laserscans aren't that common). Point-by-point pointclouds are e.g. the output of 3D lidars like Ouster (where more points can have the same timestamp, but not all). If you want to use the point-by-point mode with pointclouds, make sure they contain not only the usual x, y and z fields, but also a float32 field stamps (with time difference from the time in the header) and float32 fields vp_x, vp_y and vp_z which contain the viewpoint (position of the sensor in filtering frame) from which the robot saw that point.

When filtering in the point-by-point mode, the robot posture has to be updated several times during processing a single scan (to reflect the motion the robot has performed during acquiring the scan). The frequency of these updates can also have a significant impact on performance. Use parameter filter/model_pose_update_interval to set the interval for which the robot is considered stationary. The positions of the robot at the beginning and at the end of the scan are queried from TF. The intermediate positions are linearly interpolated between these two positions.

TF frames

The filter recognizes four logical TF frames (some of which may be the same physical frames).

Fixed frame is a frame that doesn't change within the duration of the processed scan. This means for all-at-once scans, this frame is not needed because the duration of the scan is zero. For point-by-point scans, it depends on the particular scenario. In static installations (like manipulators with sensors not attached to them), it can be the sensor frame. For stationary robots with the sensor attached to a movable part of their body, base_link will be a good choice. For completely mobile robots, you will need an external frame, e.g. odom or map (beware of cyclic dependencies - if the map is built from the filtered scans, you obviously cannot use map as the fixed frame for filtering the scans...).

Sensor frame is the frame in which the data were captured. Generally, it would be whatever is in header.frame_id field of the processed messages. You can use the filter for data from multiple sensors - in that case, you can leave the sensor frame unfilled and each message will be processed in the frame it has in its header.

Filtering frame is the frame in which the data filtering is done. For point-by-point scans, it has to be a fixed frame (possibly different from the fixed frame set in frames/fixed). For pointcloud scans, it should be the sensor frame (if all data are coming from a single sensor), or any other frame. It is also used as the frame in which all debugging outputs are published.

Output frame can only be used with pointcloud scans, and allows to transform the filtered pointcloud to a different frame before being published. It is just a convenience which can save you launching a transformation nodelet. By default, filtered pointclouds are output in the filtering frame.

Bounding shapes

As a byproduct, the filter can also compute various bounding shapes of the robot model. There are actually four robot models - one for contains test, one for shadow test, one for bounding sphere computation and one for bounding box (these models can differ by inflation and considered links). All bounding shapes are published in the filtering frame. For point-by-point scans, the bounding shapes correspond to the time instant specified in the header of the processed scan. The computation of bounding shapes is off by default, but enabling it is cheap (performance-wise).

The bounding sphere is easy - the smallest sphere that contains the whole collision model for bounding sphere computation (with the specified exclusions removed).

The bounding box is the smallest axis-aligned bounding box aligned to the filtering frame. It is built from the model for bounding box computation.

The local bounding box is the smallest axis-aligned bounding box aligned to the frame specified in local_bounding_box/frame_id. It is especially useful with mobile robots when the desired frame is base_link. It is built from the model for bounding box computation.

The oriented bounding box should be the smallest box containing the collision model. However, its computation is very bad conditioned, so the results can be very unsatisfactory. Currently, the oriented bounding box of each of the basic collision shapes is "tight", but merging the boxes is not optimal. A good algorithm would probably require costly and advanced iterative methods. The current implementation uses FCL in the background and merges the boxes using fcl::OBB::operator+=() without any further optimizations. It is built from the model for bounding box computation.

The filter also supports publishing auxiliary pointclouds which "cut out" each of these bounding shapes. These are the input data converted to pointcloud in filtering frame from which all points belonging to the bounding shape are removed. Please note that the "base" used for cutting out is the input pointcloud, not the filtered one.

First setup/debugging

The filter offers plenty of debugging outputs to make sure it does exactly what you want it to do. All the options are described in the last part of this page. Generally, you should look at the pointclouds visualizing which points got filtered out and you should also check the robot models used for filtering.

Also, have a look in the [examples] folder to get some inspiration.

Usage

This is a standard filters::FilterBase<T>-based filter which implements the configure() and update(const T&, T&) methods. This means it can be loaded e.g. as a part of a filter chain via the laser_filters package or the relatively new sensor_filters. This means the input and output data are not supplied in the form of topics, but they are instead passed to the update() method via the C++ API.

This filter is a bit unusual - it subscribes and publishes several topics. Normally, filters only operate via the update() method and are not expected to publish anything. This filter is different, it requires a working ROS node handle, and can publish a lot of auxiliary or debug data.

Subscribed Topics

  • /tf, /tf_static

    Transforms

  • dynamic_robot_model_server/parameter_updates (dynamic_reconfigure/Config)

    Dynamic reconfigure topic on which updated robot model can be subscribed. The model is read from a field defined by parameter body_model/dynamic_robot_description/field_name.

Published Topics

  • scan_point_cloud_no_bsphere (sensor_msgs/PointCloud2)

    Point cloud with points inside body bounding sphere removed. Turned on by bounding_sphere/publish_cut_out_pointcloud parameter. Published in filtering frame.

  • scan_point_cloud_no_bbox (sensor_msgs/PointCloud2)

    Point cloud with points inside filtering frame axis-aligned bounding box removed. Turned on by bounding_box/publish_cut_out_pointcloud parameter. Published in filtering frame.

  • scan_point_cloud_no_oriented_bbox (sensor_msgs/PointCloud2)

    Point cloud with points inside oriented bounding box removed. Turned on by oriented_bounding_box/publish_cut_out_pointcloud parameter. Published in filtering frame. Please read the remarks above in the overview - the results can be non-satisfying.

  • scan_point_cloud_no_local_bbox (sensor_msgs/PointCloud2)

    Point cloud with points inside local_bounding_box/frame_id axis-aligned bounding box removed. Turned on by local_bounding_box/publish_cut_out_pointcloud parameter. Published in filtering frame.

  • robot_bounding_sphere (robot_body_filter/SphereStamped)

    Bounding sphere of the robot body. Turned on by bounding_sphere/compute parameter. Published in filtering frame.

  • robot_bounding_box (geometry_msgs/PolygonStamped)

    Axis-aligned bounding box of the robot body aligned to the filtering frame. First point is the minimal point, second one is the maximal point. Turned on by bounding_box/compute parameter. Published in filtering frame.

  • robot_oriented_bounding_box (robot_body_filter/OrientedBoundingBoxStamped)

    Oriented bounding box of the robot body. Turned on by oriented_bounding_box/compute parameter. Please read the remarks above in the overview - the results can be non-satisfying. Published in filtering frame.

  • robot_local_bounding_box (geometry_msgs/PolygonStamped)

    Axis-aligned bounding box of the robot body aligned to frame local_bounding_box/frame_id. First point is the minimal point, second one is the maximal point. Turned on by local_bounding_box/compute parameter. Published in frame local_bounding_box/frame_id.

Provided Services

  • ~reload_model (std_srvs/Trigger)

    Call this service to re-read the URDF model from parameter server.

Filter Parameters

Have a look in the examples folder to get inspiration for configuration of your filter.

  • sensor/point_by_point (bool, default: false for PointCloud2 version, true for LaserScan)

    If true, suppose that every point in the scan was captured at a different time instant. Otherwise, the scan is assumed to be taken at once. If this is true, the processing pipeline expects the pointcloud to have fields int32 index, float32 stamps, and float32 vp_x, vp_y and vp_z viewpoint positions. If one of these fields is missing, computeMask() throws runtime exception.

  • frames/fixed (string, default: "base_link")

    The fixed frame. Usually base_link for stationary robots (or sensor frame if both robot and sensor are stationary). For mobile robots, it can be e.g. odom or map. Only needed for point-by-point scans.

  • frames/sensor (string, default "")

    Frame of the sensor. If set to empty string, it will be read from header.frame_id of each incoming message. In LaserScan version, if nonempty, it has to match the frame_id of the incoming scans. In PointCloud2 version, the data can come in a different frame from frames/sensor.

  • frames/filtering (string, default: frames/fixed)

    Frame in which the filter is applied. For point-by-point scans, it has to be a fixed frame, otherwise, it can be the sensor frame or any other frame. Setting to sensor frame will save some computations, but this frame cannot be empty string, so sensor frame can only be used if all data are coming from a single sensor and you know the scan frame in advance.

  • frames/output (string, default: frames/filtering)

    Frame into which output data are transformed. Only applicable in PointCloud2 version.

  • sensor/min_distance (float, default: 0.0 m)

    The minimum distance of points from the laser to keep them.

  • sensor/max_distance (float, default: 0.0 m)

    The maximum distance of points from the laser to keep them. Set to zero to disable this limit.

  • filter/keep_clouds_organized (bool, default true)

    Whether to keep pointclouds organized (if they were). Organized cloud has height > 1.

  • filter/model_pose_update_interval (float, default 0.0 s)

    The interval between two consecutive model pose updates when processing a pointByPointScan. If set to zero, the model will be updated for each point separately (might be computationally exhaustive). If non-zero, it will only update the model once in this interval, which makes the masking algorithm a little bit less precise but more computationally affordable.

  • filter/do_clipping (bool, default true)

    If true, the filter will mark points outside sensor/(min|max)_distance as CLIP. If false, such points will be marked OUTSIDE.

  • filter/do_contains_test (bool, default true)

    If true, the filter will mark points inside robot body as INSIDE. If false, such points will be marked OUTSIDE.

  • filter/do_shadow_test (bool, default true)

    If true, the filter will mark points shadowed by robot body as SHADOW. If false, such points will be marked OUTSIDE.

  • filter/max_shadow_distance (float, default is the value of sensor/max_distance)

    If greater than zero, specifies the maximum distance of a point from the sensor frame within which the point can be considered for shadow testing. All further points are classified as OUTSIDE. Setting this parameter to a low value may greatly improve performance of the shadow filtering.

  • body_model/inflation/scale (float, default 1.0)

    A scale that is applied to the collision model for the purposes of collision checking.

  • body_model/inflation/padding (float, default 0.0 m)

    Padding to be added to the collision model for the purposes of collision checking.

  • body_model/inflation/contains_test/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for contains tests.

  • body_model/inflation/contains_test/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for contains tests.

  • body_model/inflation/shadow_test/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for shadow tests.

  • body_model/inflation/shadow_test/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for shadow tests.

  • body_model/inflation/bounding_sphere/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for bounding sphere computation.

  • body_model/inflation/bounding_sphere/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for bounding sphere computation.

  • body_model/inflation/bounding_box/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for bounding box computation.

  • body_model/inflation/bounding_box/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for bounding box computation.

  • body_model/inflation/per_link/scale (dict[str:float], default {})

    A scale that is applied to the specified links for the purposes of collision checking. Links not specified here will use the default scale set in body_model/inflation/contains_test/scale or body_model/inflation/shadow_test/scale. Keys are names, values are scale. Names can be either names of links (link), names of collisions ( *::my_collision), a combination of link name and zero-based collision index (link::1), or link name and collision name, e.g. link::my_collision. Any such name can have suffix ::contains, ::shadow, ::bounding_sphere or ::bounding_box, which will only change the scale for contains or shadow tests or for bounding sphere or box computation. If a collision is matched by multiple entries, they have priority corresponding to the order they were introduced here (the entries do not "add up", but replace each other).

  • body_model/inflation/per_link/padding (dict[str:float], default {})

    Padding to be added to the specified links for the purposes of collision checking. Links not specified here will use the default padding set in body_model/inflation/contains_test/padding or body_model/inflation/shadow_test/padding. Keys are names, values are padding. Names can be either names of links (link), names of collisions ( *::my_collision), a combination of link name and zero-based collision index (link::1), or link name and collision name, e.g. link::my_collision. Any such name can have suffix ::contains, ::shadow, ::bounding_sphere or ::bounding_box, which will only change the padding for contains or shadow tests or for bounding sphere or box computation. If a collision is matched by multiple entries, they have priority corresponding to the order they were introduced here (the entries do not "add up", but replace each other).

  • body_model/robot_description_param (string, default: "robot_description")

    Name of the parameter where the robot model can be found.

  • transforms/buffer_length (float, default 60.0 s)

    Duration for which transforms will be stored in TF buffer.

  • transforms/timeout/reachable (float, default 0.1 s)

    How long to wait while getting reachable TF (i.e. transform which was previously available). Please note that this timeout is computed not from the lookup start time, but from the scan timestamp - this allows you to tell how old scans you still want to process.

  • transforms/timeout/unreachable (float, default 0.2 s)

    How long to wait while getting unreachable TF.

  • transforms/require_all_reachable (bool, default false)

If true, the filter won't publish anything until all transforms are reachable. - ignored_links/bounding_sphere (list[string], default [])

List of links to be ignored in bounding sphere computation. Can be either 
names of links (`link`), names of collisions (`*::my_collision`), a
combination of link name and zero-based collision index (`link::1`), or link
name and collision name, e.g. `link::my_collision`.
  • ignored_links/bounding_box (list[string], default [])

    List of links to be ignored in bounding box computation. Same naming rules as above.

  • ignored_links/contains_test (list[string], default [])

    List of links to be ignored when testing if a point is inside robot body. Same naming rules as above.

  • ignored_links/shadow_test (list[string], default ['laser'])

    List of links to be ignored when testing if a point is shadowed by robot body. Same naming rules as above. It is essential that this list contains the sensor link - otherwise all points would be shadowed by the sensor itself.

  • ignored_links/bounding_sphere (list[string], default [])

    List of links to be ignored when computing the bounding sphere. Same naming rules as above.

  • ignored_links/bounding_box (list[string], default [])

    List of links to be ignored when computing the bounding box. Same naming rules as above.

  • ignored_links/everywhere (list[string], default [])

    List of links to be completely ignored. Same naming rules as above.

  • only_links (list[string], default [])

    If non-empty, only the specified links will be processed. The above-mentioned ignored_links/* filters will still be applied.

  • bounding_sphere/compute (bool, default false)

    Whether to compute and publish bounding sphere.

  • bounding_box/compute (bool, default false)

    Whether to compute and publish axis-aligned bounding box aligned to filtering frame.

  • oriented_bounding_box/compute (bool, default false)

    Whether to compute and publish oriented bounding box (see the remarks in the overview above; the result might be pretty bad).

  • local_bounding_box/compute (bool, default false)

    Whether to compute and publish axis-aligned bounding box aligned to frame local_bounding_box/frame_id.

  • local_bounding_box/frame_id (str, default: frames/fixed)

    The frame to which local bounding box is aligned.

  • bounding_sphere/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the bounding sphere are removed. Will be published on scan_point_cloud_no_bsphere. Implies bounding_sphere/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • bounding_box/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the bounding box are removed. Will be published on scan_point_cloud_no_bbox. Implies bounding_box/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • oriented_bounding_box/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the oriented bounding box are removed. Will be published on scan_point_cloud_no_oriented_bbox. Implies oriented_bounding_box/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • local_bounding_box/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the local bounding box are removed. Will be published on scan_point_cloud_no_local_bbox. Implies local_bounding_box/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • body_model/dynamic_robot_description/field_name (string, default robot_model)

    If robot model is published by dynamic reconfigure, this is the name of the Config message field which holds the robot model.

  • cloud/point_channels (list[string], default ["vp_"])

    List of channels of the incoming pointcloud that should be transformed as positional data. The 3D positions channel given by fields x, y and z is always transformed. This list contains prefixes of fields that form another channel(s). E.g. to transform the channel given by fields vp_x, vp_y and vp_z, add item "vp_" to the list. If a channel is not present in the cloud, nothing happens. This parameter is only available in the PointCloud2 version of the filter.

  • cloud/direction_channels (list[string], default ["normal_"])

    List of channels of the incoming pointcloud that should be transformed as directional data. This list contains prefixes of fields that form channel(s). E.g. to transform the channel given by fields normal_x, normal_y and normal_z, add item "normal_" to the list. If a channel is not present in the cloud, nothing happens. This parameter is only available in the PointCloud2 version of the filter.

Debug Operation

These options are there to help correctly set up and debug the filter operation and should be turned off in production environments since they can degrade performance of the filter.

Published Topics

  • robot_bounding_sphere_marker (visualization_msgs/Marker)

    Marker of the bounding sphere of the robot body. Turned on by bounding_sphere/marker parameter. Published in filtering frame.

  • robot_bounding_box_marker (visualization_msgs/Marker)

    Marker of the bounding box of the robot body. Turned on by bounding_box/marker parameter. Published in filtering frame.

  • robot_oriented_bounding_box_marker (visualization_msgs/Marker)

    Marker of the oriented bounding box of the robot body. Turned on by oriented_bounding_box/marker parameter. Published in filtering frame.

  • robot_local_bounding_box_marker (visualization_msgs/Marker)

    Marker of the local bounding box of the robot body. Turned on by local bounding_box/marker parameter. Published in frame local_bounding_box/frame_id.

  • robot_bounding_sphere_debug (visualization_msgs/MarkerArray)

    Marker array containing the bounding sphere for each collision element. Turned on by bounding_sphere/debug parameter. Published in filtering frame.

  • robot_bounding_box_debug (visualization_msgs/MarkerArray)

    Marker array containing the bounding box for each collision element. Turned on by bounding_box/debug parameter. Published in filtering frame.

  • robot_oriented_bounding_box_debug (visualization_msgs/MarkerArray)

    Marker array containing the oriented bounding box for each collision element. Turned on by oriented_bounding_box/debug parameter. Published in filtering frame.

  • robot_local_bounding_box_debug (visualization_msgs/MarkerArray)

    Marker array containing the local bounding box for each collision element. Turned on by local_bounding_box/debug parameter. Published in frame local_bounding_box/frame_id.

  • robot_model_for_contains_test (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for contains tests. Turned on by debug/marker/contains parameter.

  • robot_model_for_shadow_test (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for shadow tests. Turned on by debug/marker/shadow parameter.

  • robot_model_for_bounding_sphere (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for computation of bounding sphere. Turned on by debug/marker/bounding_sphere parameter.

  • robot_model_for_bounding_box (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for computation of bounding box. Turned on by debug/marker/bounding_box parameter.

  • scan_point_cloud_inside (sensor_msgs/PointCloud2)

    Debugging pointcloud with points classified as INSIDE. Turned on by debug/pcl/inside parameter.

  • scan_point_cloud_clip (sensor_msgs/PointCloud2)

    Debugging pointcloud with points classified as CLIP. Turned on by debug/pcl/clip parameter.

  • scan_point_cloud_shadow (sensor_msgs/PointCloud2)

    Debugging pointcloud with points classified as SHADOW. Turned on by debug/pcl/shadow parameter.

Filter Parameters

  • bounding_sphere/marker (bool, default false)

    Whether to publish a marker representing the bounding sphere.

  • bounding_box/marker (bool, default false)

    Whether to publish a marker representing the axis-aligned bounding box aligned to the filtering frame.

  • oriented_bounding_box/marker (bool, default false)

    Whether to publish a marker representing the oriented bounding box.

  • local_bounding_box/marker (bool, default false)

    Whether to publish a marker representing the axis-aligned bounding box aligned to frame local_bounding_box/frame_id.

  • bounding_sphere/debug (bool, default false)

    Whether to compute and publish debug bounding spheres (marker array of spheres for each collision).

  • bounding_box/debug (bool, default false)

    Whether to compute and publish debug bounding boxes (marker array of axis-aligned boxes for each collision).

  • oriented_bounding_box/debug (bool, default false)

    Whether to compute and publish debug oriented bounding boxes (marker array of oriented boxes for each collision).

  • local_bounding_box/debug (bool, default false)

    Whether to compute and publish debug axis-aligned bounding boxes aligned to frame local_bounding_box/frame_id (marker array of axis-aligned boxes for each collision).

  • debug/pcl/inside (bool, default false)

    Whether to publish debugging pointcloud with points marked as INSIDE.

  • debug/pcl/clip (bool, default false)

    Whether to publish debugging pointcloud with points marked as CLIP.

  • debug/pcl/shadow (bool, default false)

    Whether to publish debugging pointcloud with points marked as SHADOW.

  • debug/marker/contains (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for containment test.

  • debug/marker/shadow (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for shadow test.

  • debug/marker/bounding_sphere (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for computing the bounding sphere.

  • debug/marker/bounding_box (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for computing the bounding box.

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/peci1/robot_body_filter.git
VCS Type git
VCS Version master
Last Updated 2021-07-30
Dev Status MAINTAINED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
robot_body_filter 1.2.0

README

robot_body_filter

Filters the robot's body out of point clouds and laser scans.

Changes vs PR2/robot_self_filter:

  • Now the package is a normal filters::FilterBase filter and not a standalone node.
  • Using both containment and ray-tracing tests.
  • Using all collision elements for each link instead of only the first one.
  • Enabling generic point type, removing PCL dependency and unnecessary params.
  • Using bodies.h and shapes.h from geometric_shapes.
  • As a by-product, the filter can compute robot's bounding box or sphere.

Build Status

Continuous integration

ROS Version Build Status
Melodic Build Status
Build Status

Dev job

ROS Version Build Status
Melodic Build Status
Noetic Build Status

Release jobs

ROS Version Ubuntu amd64 Ubuntu armhf Ubuntu arm64 Debian amd64 Debian arm64
Melodic Bionic Build Status Bionic Build Status Bionic Build Status Stretch Build Status Stretch Build Status
Noetic Focal Build Status Focal Build Status Focal Build Status Buster Build Status Buster Build Status

Basic Operation

filters::FilterBase API

The basic workings of this filter are done via the filters::FilterBase API implemented for sensor_msgs::LaserScan and sensor_msgs::PointCloud2 types. This means you can load this filter into a FilterChain along other filters as usual. Different from the standard filters, this one can also publish several interesting topics and subscribes to TF.

General overview

This filter reads robot model and the filter config, subscribes to TF, waits for data (laserscans or point clouds) and then cleans them from various artifacts (this is called data filtering).

It can perform 3 kinds of data filters: clip the data based on the provided sensor limits (parameter filter/do_clipping), remove points that are inside or on the surface of the robot body (parameter filter/do_contains_test) and remove points that are seen through a part of the robot body (parameter filter/do_shadow_test). These kinds of tests are further referenced as "clipping", "contains test" and "shadow test".

If working with point clouds, the filter automatically recognizes whether it works with organized or non-organized clouds. In organized clouds, it marks the filtered-out points as NaN. In non-organized clouds, it removes the filtered-out points. In laserscans, removal is not an option, so the filtered-out points are marked with NaN (some guides suggest that max_range + 1 should be used for marking invalid points, but this filter uses NaN as a safer option).

Performance tips

In general, the filter will be computationally expensive (clipping is fast, contains test is medium CPU intensive and shadow test is the most expensive part, because it basically performs raytracing). You can limit the required CPU power by limiting the filter only to parts that matter. E.g. if the robot has a link that can never be seen by the sensor, put it in the list of ignored links. The less links are processed, the better performance. If you're only interested in removing a few links, consider using the only_links parameter.

To speed up shadow filtering, you can set filter/max_shadow_distance, which limits the number of points considered for shadow tests just to points close to the sensor. Setting this to e.g. three times the diameter of the robot should remove all of the shadow points caused by refraction by a part of the robot body. But you have to test this with real data.

Performance also strongly depends on representation of the robot model. The filter reads <collision> tags from the robot URDF. You can use boxes, spheres and cylinders (which are fast to process), or you can use convex meshes (these are much worse performance-wise). If you pass a non-convex mesh, its convex hull will be used for the tests. Don't forget that each link can have multiple <collision> tags. If you do not have time to convert your meshes to the basic shapes (there is probably no good tool for it :(, just trial and error with restarting RViz), try to at least reduce the number of triangles in your meshes. You can use your high-quality meshes in <visual> tags.

Model inflation

You can utilize the builtin model inflation mechanism to slightly alter the size of the model. You will probably want to add a bit "margin" to the contains and shadow tests so that points that are millimeters outside the robot body will anyways get removed. You can set a default scale and padding which are used for all collisions. Different inflation can be used for contains tests and for shadow tests. Inflation can also be specified differently for each link. Look at the body_model/inflation/* parameters for details.

Scaling means multiplying the shape dimensions by the given factor (with its center staying in the same place). Padding means adding the specified metric distance to each "dimension" of the shape. Padding a sphere by p just adds p to its radius; padding a cylinder adds p to its radius and 2p to its length (you pad both the top and the bottom of the cylinder); padding a box adds 2p to all its extents (again, you pad both of the opposing sides); padding a mesh pads each vertex of its convex hull by p along the direction from the mesh center to the vertex (mesh center is the center specified in the mesh file, e.g. DAE). Have a good look at the effects of mesh padding, as the results can be non-intuitive.

Data acquisition modes

The filter supports data captured in two modes - all at once (e.g. RGBD cameras), or each point at a different time instant (mostly lidars). This is handled by sensor/point_by_point setting. Each mode supports both laser scans and point cloud input (although all-at-once laserscans aren't that common). Point-by-point pointclouds are e.g. the output of 3D lidars like Ouster (where more points can have the same timestamp, but not all). If you want to use the point-by-point mode with pointclouds, make sure they contain not only the usual x, y and z fields, but also a float32 field stamps (with time difference from the time in the header) and float32 fields vp_x, vp_y and vp_z which contain the viewpoint (position of the sensor in filtering frame) from which the robot saw that point.

When filtering in the point-by-point mode, the robot posture has to be updated several times during processing a single scan (to reflect the motion the robot has performed during acquiring the scan). The frequency of these updates can also have a significant impact on performance. Use parameter filter/model_pose_update_interval to set the interval for which the robot is considered stationary. The positions of the robot at the beginning and at the end of the scan are queried from TF. The intermediate positions are linearly interpolated between these two positions.

TF frames

The filter recognizes four logical TF frames (some of which may be the same physical frames).

Fixed frame is a frame that doesn't change within the duration of the processed scan. This means for all-at-once scans, this frame is not needed because the duration of the scan is zero. For point-by-point scans, it depends on the particular scenario. In static installations (like manipulators with sensors not attached to them), it can be the sensor frame. For stationary robots with the sensor attached to a movable part of their body, base_link will be a good choice. For completely mobile robots, you will need an external frame, e.g. odom or map (beware of cyclic dependencies - if the map is built from the filtered scans, you obviously cannot use map as the fixed frame for filtering the scans...).

Sensor frame is the frame in which the data were captured. Generally, it would be whatever is in header.frame_id field of the processed messages. You can use the filter for data from multiple sensors - in that case, you can leave the sensor frame unfilled and each message will be processed in the frame it has in its header.

Filtering frame is the frame in which the data filtering is done. For point-by-point scans, it has to be a fixed frame (possibly different from the fixed frame set in frames/fixed). For pointcloud scans, it should be the sensor frame (if all data are coming from a single sensor), or any other frame. It is also used as the frame in which all debugging outputs are published.

Output frame can only be used with pointcloud scans, and allows to transform the filtered pointcloud to a different frame before being published. It is just a convenience which can save you launching a transformation nodelet. By default, filtered pointclouds are output in the filtering frame.

Bounding shapes

As a byproduct, the filter can also compute various bounding shapes of the robot model. There are actually four robot models - one for contains test, one for shadow test, one for bounding sphere computation and one for bounding box (these models can differ by inflation and considered links). All bounding shapes are published in the filtering frame. For point-by-point scans, the bounding shapes correspond to the time instant specified in the header of the processed scan. The computation of bounding shapes is off by default, but enabling it is cheap (performance-wise).

The bounding sphere is easy - the smallest sphere that contains the whole collision model for bounding sphere computation (with the specified exclusions removed).

The bounding box is the smallest axis-aligned bounding box aligned to the filtering frame. It is built from the model for bounding box computation.

The local bounding box is the smallest axis-aligned bounding box aligned to the frame specified in local_bounding_box/frame_id. It is especially useful with mobile robots when the desired frame is base_link. It is built from the model for bounding box computation.

The oriented bounding box should be the smallest box containing the collision model. However, its computation is very bad conditioned, so the results can be very unsatisfactory. Currently, the oriented bounding box of each of the basic collision shapes is "tight", but merging the boxes is not optimal. A good algorithm would probably require costly and advanced iterative methods. The current implementation uses FCL in the background and merges the boxes using fcl::OBB::operator+=() without any further optimizations. It is built from the model for bounding box computation.

The filter also supports publishing auxiliary pointclouds which "cut out" each of these bounding shapes. These are the input data converted to pointcloud in filtering frame from which all points belonging to the bounding shape are removed. Please note that the "base" used for cutting out is the input pointcloud, not the filtered one.

First setup/debugging

The filter offers plenty of debugging outputs to make sure it does exactly what you want it to do. All the options are described in the last part of this page. Generally, you should look at the pointclouds visualizing which points got filtered out and you should also check the robot models used for filtering.

Also, have a look in the [examples] folder to get some inspiration.

Usage

This is a standard filters::FilterBase<T>-based filter which implements the configure() and update(const T&, T&) methods. This means it can be loaded e.g. as a part of a filter chain via the laser_filters package or the relatively new sensor_filters. This means the input and output data are not supplied in the form of topics, but they are instead passed to the update() method via the C++ API.

This filter is a bit unusual - it subscribes and publishes several topics. Normally, filters only operate via the update() method and are not expected to publish anything. This filter is different, it requires a working ROS node handle, and can publish a lot of auxiliary or debug data.

Subscribed Topics

  • /tf, /tf_static

    Transforms

  • dynamic_robot_model_server/parameter_updates (dynamic_reconfigure/Config)

    Dynamic reconfigure topic on which updated robot model can be subscribed. The model is read from a field defined by parameter body_model/dynamic_robot_description/field_name.

Published Topics

  • scan_point_cloud_no_bsphere (sensor_msgs/PointCloud2)

    Point cloud with points inside body bounding sphere removed. Turned on by bounding_sphere/publish_cut_out_pointcloud parameter. Published in filtering frame.

  • scan_point_cloud_no_bbox (sensor_msgs/PointCloud2)

    Point cloud with points inside filtering frame axis-aligned bounding box removed. Turned on by bounding_box/publish_cut_out_pointcloud parameter. Published in filtering frame.

  • scan_point_cloud_no_oriented_bbox (sensor_msgs/PointCloud2)

    Point cloud with points inside oriented bounding box removed. Turned on by oriented_bounding_box/publish_cut_out_pointcloud parameter. Published in filtering frame. Please read the remarks above in the overview - the results can be non-satisfying.

  • scan_point_cloud_no_local_bbox (sensor_msgs/PointCloud2)

    Point cloud with points inside local_bounding_box/frame_id axis-aligned bounding box removed. Turned on by local_bounding_box/publish_cut_out_pointcloud parameter. Published in filtering frame.

  • robot_bounding_sphere (robot_body_filter/SphereStamped)

    Bounding sphere of the robot body. Turned on by bounding_sphere/compute parameter. Published in filtering frame.

  • robot_bounding_box (geometry_msgs/PolygonStamped)

    Axis-aligned bounding box of the robot body aligned to the filtering frame. First point is the minimal point, second one is the maximal point. Turned on by bounding_box/compute parameter. Published in filtering frame.

  • robot_oriented_bounding_box (robot_body_filter/OrientedBoundingBoxStamped)

    Oriented bounding box of the robot body. Turned on by oriented_bounding_box/compute parameter. Please read the remarks above in the overview - the results can be non-satisfying. Published in filtering frame.

  • robot_local_bounding_box (geometry_msgs/PolygonStamped)

    Axis-aligned bounding box of the robot body aligned to frame local_bounding_box/frame_id. First point is the minimal point, second one is the maximal point. Turned on by local_bounding_box/compute parameter. Published in frame local_bounding_box/frame_id.

Provided Services

  • ~reload_model (std_srvs/Trigger)

    Call this service to re-read the URDF model from parameter server.

Filter Parameters

Have a look in the examples folder to get inspiration for configuration of your filter.

  • sensor/point_by_point (bool, default: false for PointCloud2 version, true for LaserScan)

    If true, suppose that every point in the scan was captured at a different time instant. Otherwise, the scan is assumed to be taken at once. If this is true, the processing pipeline expects the pointcloud to have fields int32 index, float32 stamps, and float32 vp_x, vp_y and vp_z viewpoint positions. If one of these fields is missing, computeMask() throws runtime exception.

  • frames/fixed (string, default: "base_link")

    The fixed frame. Usually base_link for stationary robots (or sensor frame if both robot and sensor are stationary). For mobile robots, it can be e.g. odom or map. Only needed for point-by-point scans.

  • frames/sensor (string, default "")

    Frame of the sensor. If set to empty string, it will be read from header.frame_id of each incoming message. In LaserScan version, if nonempty, it has to match the frame_id of the incoming scans. In PointCloud2 version, the data can come in a different frame from frames/sensor.

  • frames/filtering (string, default: frames/fixed)

    Frame in which the filter is applied. For point-by-point scans, it has to be a fixed frame, otherwise, it can be the sensor frame or any other frame. Setting to sensor frame will save some computations, but this frame cannot be empty string, so sensor frame can only be used if all data are coming from a single sensor and you know the scan frame in advance.

  • frames/output (string, default: frames/filtering)

    Frame into which output data are transformed. Only applicable in PointCloud2 version.

  • sensor/min_distance (float, default: 0.0 m)

    The minimum distance of points from the laser to keep them.

  • sensor/max_distance (float, default: 0.0 m)

    The maximum distance of points from the laser to keep them. Set to zero to disable this limit.

  • filter/keep_clouds_organized (bool, default true)

    Whether to keep pointclouds organized (if they were). Organized cloud has height > 1.

  • filter/model_pose_update_interval (float, default 0.0 s)

    The interval between two consecutive model pose updates when processing a pointByPointScan. If set to zero, the model will be updated for each point separately (might be computationally exhaustive). If non-zero, it will only update the model once in this interval, which makes the masking algorithm a little bit less precise but more computationally affordable.

  • filter/do_clipping (bool, default true)

    If true, the filter will mark points outside sensor/(min|max)_distance as CLIP. If false, such points will be marked OUTSIDE.

  • filter/do_contains_test (bool, default true)

    If true, the filter will mark points inside robot body as INSIDE. If false, such points will be marked OUTSIDE.

  • filter/do_shadow_test (bool, default true)

    If true, the filter will mark points shadowed by robot body as SHADOW. If false, such points will be marked OUTSIDE.

  • filter/max_shadow_distance (float, default is the value of sensor/max_distance)

    If greater than zero, specifies the maximum distance of a point from the sensor frame within which the point can be considered for shadow testing. All further points are classified as OUTSIDE. Setting this parameter to a low value may greatly improve performance of the shadow filtering.

  • body_model/inflation/scale (float, default 1.0)

    A scale that is applied to the collision model for the purposes of collision checking.

  • body_model/inflation/padding (float, default 0.0 m)

    Padding to be added to the collision model for the purposes of collision checking.

  • body_model/inflation/contains_test/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for contains tests.

  • body_model/inflation/contains_test/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for contains tests.

  • body_model/inflation/shadow_test/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for shadow tests.

  • body_model/inflation/shadow_test/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for shadow tests.

  • body_model/inflation/bounding_sphere/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for bounding sphere computation.

  • body_model/inflation/bounding_sphere/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for bounding sphere computation.

  • body_model/inflation/bounding_box/scale (float, default body_model/inflation/scale)

    A scale that is applied to the collision model used for bounding box computation.

  • body_model/inflation/bounding_box/padding (float, default body_model/inflation/padding)

    Padding to be added to the collision model used for bounding box computation.

  • body_model/inflation/per_link/scale (dict[str:float], default {})

    A scale that is applied to the specified links for the purposes of collision checking. Links not specified here will use the default scale set in body_model/inflation/contains_test/scale or body_model/inflation/shadow_test/scale. Keys are names, values are scale. Names can be either names of links (link), names of collisions ( *::my_collision), a combination of link name and zero-based collision index (link::1), or link name and collision name, e.g. link::my_collision. Any such name can have suffix ::contains, ::shadow, ::bounding_sphere or ::bounding_box, which will only change the scale for contains or shadow tests or for bounding sphere or box computation. If a collision is matched by multiple entries, they have priority corresponding to the order they were introduced here (the entries do not "add up", but replace each other).

  • body_model/inflation/per_link/padding (dict[str:float], default {})

    Padding to be added to the specified links for the purposes of collision checking. Links not specified here will use the default padding set in body_model/inflation/contains_test/padding or body_model/inflation/shadow_test/padding. Keys are names, values are padding. Names can be either names of links (link), names of collisions ( *::my_collision), a combination of link name and zero-based collision index (link::1), or link name and collision name, e.g. link::my_collision. Any such name can have suffix ::contains, ::shadow, ::bounding_sphere or ::bounding_box, which will only change the padding for contains or shadow tests or for bounding sphere or box computation. If a collision is matched by multiple entries, they have priority corresponding to the order they were introduced here (the entries do not "add up", but replace each other).

  • body_model/robot_description_param (string, default: "robot_description")

    Name of the parameter where the robot model can be found.

  • transforms/buffer_length (float, default 60.0 s)

    Duration for which transforms will be stored in TF buffer.

  • transforms/timeout/reachable (float, default 0.1 s)

    How long to wait while getting reachable TF (i.e. transform which was previously available). Please note that this timeout is computed not from the lookup start time, but from the scan timestamp - this allows you to tell how old scans you still want to process.

  • transforms/timeout/unreachable (float, default 0.2 s)

    How long to wait while getting unreachable TF.

  • transforms/require_all_reachable (bool, default false)

If true, the filter won't publish anything until all transforms are reachable. - ignored_links/bounding_sphere (list[string], default [])

List of links to be ignored in bounding sphere computation. Can be either 
names of links (`link`), names of collisions (`*::my_collision`), a
combination of link name and zero-based collision index (`link::1`), or link
name and collision name, e.g. `link::my_collision`.
  • ignored_links/bounding_box (list[string], default [])

    List of links to be ignored in bounding box computation. Same naming rules as above.

  • ignored_links/contains_test (list[string], default [])

    List of links to be ignored when testing if a point is inside robot body. Same naming rules as above.

  • ignored_links/shadow_test (list[string], default ['laser'])

    List of links to be ignored when testing if a point is shadowed by robot body. Same naming rules as above. It is essential that this list contains the sensor link - otherwise all points would be shadowed by the sensor itself.

  • ignored_links/bounding_sphere (list[string], default [])

    List of links to be ignored when computing the bounding sphere. Same naming rules as above.

  • ignored_links/bounding_box (list[string], default [])

    List of links to be ignored when computing the bounding box. Same naming rules as above.

  • ignored_links/everywhere (list[string], default [])

    List of links to be completely ignored. Same naming rules as above.

  • only_links (list[string], default [])

    If non-empty, only the specified links will be processed. The above-mentioned ignored_links/* filters will still be applied.

  • bounding_sphere/compute (bool, default false)

    Whether to compute and publish bounding sphere.

  • bounding_box/compute (bool, default false)

    Whether to compute and publish axis-aligned bounding box aligned to filtering frame.

  • oriented_bounding_box/compute (bool, default false)

    Whether to compute and publish oriented bounding box (see the remarks in the overview above; the result might be pretty bad).

  • local_bounding_box/compute (bool, default false)

    Whether to compute and publish axis-aligned bounding box aligned to frame local_bounding_box/frame_id.

  • local_bounding_box/frame_id (str, default: frames/fixed)

    The frame to which local bounding box is aligned.

  • bounding_sphere/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the bounding sphere are removed. Will be published on scan_point_cloud_no_bsphere. Implies bounding_sphere/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • bounding_box/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the bounding box are removed. Will be published on scan_point_cloud_no_bbox. Implies bounding_box/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • oriented_bounding_box/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the oriented bounding box are removed. Will be published on scan_point_cloud_no_oriented_bbox. Implies oriented_bounding_box/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • local_bounding_box/publish_cut_out_pointcloud (bool, default false)

    Whether to compute and publish pointcloud from which points in the local bounding box are removed. Will be published on scan_point_cloud_no_local_bbox. Implies local_bounding_box/compute. The "base" point cloud before cutting out are the input data, not the filtered data.

  • body_model/dynamic_robot_description/field_name (string, default robot_model)

    If robot model is published by dynamic reconfigure, this is the name of the Config message field which holds the robot model.

  • cloud/point_channels (list[string], default ["vp_"])

    List of channels of the incoming pointcloud that should be transformed as positional data. The 3D positions channel given by fields x, y and z is always transformed. This list contains prefixes of fields that form another channel(s). E.g. to transform the channel given by fields vp_x, vp_y and vp_z, add item "vp_" to the list. If a channel is not present in the cloud, nothing happens. This parameter is only available in the PointCloud2 version of the filter.

  • cloud/direction_channels (list[string], default ["normal_"])

    List of channels of the incoming pointcloud that should be transformed as directional data. This list contains prefixes of fields that form channel(s). E.g. to transform the channel given by fields normal_x, normal_y and normal_z, add item "normal_" to the list. If a channel is not present in the cloud, nothing happens. This parameter is only available in the PointCloud2 version of the filter.

Debug Operation

These options are there to help correctly set up and debug the filter operation and should be turned off in production environments since they can degrade performance of the filter.

Published Topics

  • robot_bounding_sphere_marker (visualization_msgs/Marker)

    Marker of the bounding sphere of the robot body. Turned on by bounding_sphere/marker parameter. Published in filtering frame.

  • robot_bounding_box_marker (visualization_msgs/Marker)

    Marker of the bounding box of the robot body. Turned on by bounding_box/marker parameter. Published in filtering frame.

  • robot_oriented_bounding_box_marker (visualization_msgs/Marker)

    Marker of the oriented bounding box of the robot body. Turned on by oriented_bounding_box/marker parameter. Published in filtering frame.

  • robot_local_bounding_box_marker (visualization_msgs/Marker)

    Marker of the local bounding box of the robot body. Turned on by local bounding_box/marker parameter. Published in frame local_bounding_box/frame_id.

  • robot_bounding_sphere_debug (visualization_msgs/MarkerArray)

    Marker array containing the bounding sphere for each collision element. Turned on by bounding_sphere/debug parameter. Published in filtering frame.

  • robot_bounding_box_debug (visualization_msgs/MarkerArray)

    Marker array containing the bounding box for each collision element. Turned on by bounding_box/debug parameter. Published in filtering frame.

  • robot_oriented_bounding_box_debug (visualization_msgs/MarkerArray)

    Marker array containing the oriented bounding box for each collision element. Turned on by oriented_bounding_box/debug parameter. Published in filtering frame.

  • robot_local_bounding_box_debug (visualization_msgs/MarkerArray)

    Marker array containing the local bounding box for each collision element. Turned on by local_bounding_box/debug parameter. Published in frame local_bounding_box/frame_id.

  • robot_model_for_contains_test (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for contains tests. Turned on by debug/marker/contains parameter.

  • robot_model_for_shadow_test (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for shadow tests. Turned on by debug/marker/shadow parameter.

  • robot_model_for_bounding_sphere (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for computation of bounding sphere. Turned on by debug/marker/bounding_sphere parameter.

  • robot_model_for_bounding_box (visualization_msgs/MarkerArray)

    Marker array containing the exact robot model used for computation of bounding box. Turned on by debug/marker/bounding_box parameter.

  • scan_point_cloud_inside (sensor_msgs/PointCloud2)

    Debugging pointcloud with points classified as INSIDE. Turned on by debug/pcl/inside parameter.

  • scan_point_cloud_clip (sensor_msgs/PointCloud2)

    Debugging pointcloud with points classified as CLIP. Turned on by debug/pcl/clip parameter.

  • scan_point_cloud_shadow (sensor_msgs/PointCloud2)

    Debugging pointcloud with points classified as SHADOW. Turned on by debug/pcl/shadow parameter.

Filter Parameters

  • bounding_sphere/marker (bool, default false)

    Whether to publish a marker representing the bounding sphere.

  • bounding_box/marker (bool, default false)

    Whether to publish a marker representing the axis-aligned bounding box aligned to the filtering frame.

  • oriented_bounding_box/marker (bool, default false)

    Whether to publish a marker representing the oriented bounding box.

  • local_bounding_box/marker (bool, default false)

    Whether to publish a marker representing the axis-aligned bounding box aligned to frame local_bounding_box/frame_id.

  • bounding_sphere/debug (bool, default false)

    Whether to compute and publish debug bounding spheres (marker array of spheres for each collision).

  • bounding_box/debug (bool, default false)

    Whether to compute and publish debug bounding boxes (marker array of axis-aligned boxes for each collision).

  • oriented_bounding_box/debug (bool, default false)

    Whether to compute and publish debug oriented bounding boxes (marker array of oriented boxes for each collision).

  • local_bounding_box/debug (bool, default false)

    Whether to compute and publish debug axis-aligned bounding boxes aligned to frame local_bounding_box/frame_id (marker array of axis-aligned boxes for each collision).

  • debug/pcl/inside (bool, default false)

    Whether to publish debugging pointcloud with points marked as INSIDE.

  • debug/pcl/clip (bool, default false)

    Whether to publish debugging pointcloud with points marked as CLIP.

  • debug/pcl/shadow (bool, default false)

    Whether to publish debugging pointcloud with points marked as SHADOW.

  • debug/marker/contains (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for containment test.

  • debug/marker/shadow (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for shadow test.

  • debug/marker/bounding_sphere (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for computing the bounding sphere.

  • debug/marker/bounding_box (bool, default false)

    Whether to publish debugging marker array containing the exact robot body model used for computing the bounding box.

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/peci1/robot_body_filter.git
VCS Type git
VCS Version indigo-devel
Last Updated 2019-03-27
Dev Status MAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
robot_self_filter 0.1.31

README

robot_self_filter Build Status

Added Indigo devel

CONTRIBUTING

No CONTRIBUTING.md found.