sensor_filters repository

Repository Summary

Checkout URI https://github.com/ctu-vras/sensor_filters.git
VCS Type git
VCS Version master
Last Updated 2022-08-15
Dev Status DEVELOPED
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
sensor_filters 1.0.5

README

sensor_filters

This package is a collection of nodes and nodelets that service a filters::FilterChain for message types from sensor_msgs package. See https://wiki.ros.org/filters to read more about the filters package.

Attention: The PCL filters provided by package pcl_ros are not "compatible" with this package. They are instances of PCL filters, but written as ROS nodelets, not as ROS filters implementing the filters::FilterBase<> interface required by this package. Running pcl_ros filter nodelets does not require any additional setup except a running nodelet manager.

The task of each node(let) in sensor_filters is very simple: load the filter chain, subscribe ~input topic, and publish the filtered messages to ~output topic.

The size of message queues is configured via ~input_queue_size and ~output_queue_size, both defaulting to 10 messages.

The namespaces from which the filters load are the following:

Message Type Namespace Nodelet Name
CompressedImage ~image_filter_chain sensor_filters/compressedimage_filter_chain
Image ~image_filter_chain sensor_filters/image_filter_chain
Imu ~imu_filter_chain sensor_filters/imu_filter_chain
Joy ~joy_filter_chain sensor_filters/joy_filter_chain
LaserScan ~scan_filter_chain sensor_filters/laserscan_filter_chain
MagneticField ~magnetic_field_filter_chain sensor_filters/magneticfield_filter_chain
MultiEchoLaserScan ~scan_filter_chain sensor_filters/multiecholaserscan_filter_chain
NavSatFix ~nav_sat_fix_filter_chain sensor_filters/navsatfix_filter_chain
PointCloud ~cloud_filter_chain sensor_filters/pointcloud_filter_chain
PointCloud2 ~cloud_filter_chain sensor_filters/pointcloud2_filter_chain
Range ~range_filter_chain sensor_filters/range_filter_chain
RelativeHumidity ~humidity_filter_chain sensor_filters/relativehumidity_filter_chain
Temperature ~temperature_filter_chain sensor_filters/temperature_filter_chain

Provided filters

This package also provides a few high-level filters.

sensor_filters/ChangeHeader/$MESSAGE_TYPE

This filter allows changing the contents of the header of sensor messages. You can use it for example to correct the frame_id of a sensor, or adjust the timestamp of its messages.

Parameters

  • frame_id_prefix (string): Add this prefix to header.frame_id
  • frame_id_suffix (string): Add this suffix to header.frame_id
  • frame_id (string): Replace header.frame_idwith this value (prefix and suffix are then ignored)
  • seq (uint): Replace header.seq with this value
  • seq_relative (uint): Add this value to header.seq (watch out for unsigned int underflow)
  • stamp (double): Set header.stamp to this value (double value is converted to ros::Time)
  • stamp_relative (double): Add this value to header.stamp (double value is converted to ros::Duration)

Where to get other filters

If you are looking for other implementations of sensor filters, you will probably be disappointed, as they are really scarce. I don't know why it is the case, because filter chains are the most efficient data processing path ROS offers, yet most people chose to write nodelets instead.

There is, however, one noteworthy exception:

robot_body_filter

robot_body_filter is a versatile tool for removing the body parts of a robot from laser scans and point clouds exactly according to the robot's URDF model. No more box approximations of your robots! Represent them exactly as they are!

laser_filters compatibility

The LaserScan filter chain node is compatible with the scan_to_scan_node from laser_filters package (it can load the same filters using the same config). It does not, however, use TF message filter, so each filter has to wait for the required TFs itself.

Why ROS filters?

The simple answer is - performance. ROS filters are the most efficient way to run a chain of processors on sensor data. Passing the message from one filter to another is as simple as doing a C++ object copy. There is even a proposal for an in-place (zero-copy) implementation.

You can think about nodelets and their said performance. If used right and you run only non-modifying filters, you might actually get to true zero-copy filtering. But you have a dynamic allocation of at least one shared_ptr in your path, which might be costly and with unpredictable impacts on run time.

If you use nodelets and publish/subscribe references to messages instead of shared_ptrs, passing the message from one nodelet to another in the same manager not only does a copy of the message, but it also undergoes a serialization and deserialization step, which is most probably costly.

If you do not even use nodelets, but plain nodes, passing a message requires serializing it and sending via a local (or, even worse, remote TCP) socket to the other node. So you have copying, (de)serializing and sending via the network stack.

In the future, I plan to add some measurements to give some solid ground to these propositions.

Extensibility

Both the nodes and nodelets offer common code that can be shared so that you can build extensions of the simple filter chain handlers provided by this package.

See the examples folder for more verbose examples.

Nodes

#include <sensor_msgs/PointCloud2.h>
#include <sensor_filters/FilterChainNode.h>

class MyClass : public sensor_filters::FilterChainNode<sensor_msgs::PointCloud2>
{
  // custom class code
};

Nodelets

#include <pluginlib/class_list_macros.h>
#include <sensor_msgs/PointCloud2.h>
#include <sensor_filters/FilterChainNodelet.h>

class MyNodelet : public sensor_filters::FilterChainNodelet<sensor_msgs::PointCloud2>
{
  protected: void onInit() override
  {
    sensor_filters::FilterChainNodelet<sensor_msgs::PointCloud2>::onInit();
    // custom nodelet code
  }
};

PLUGINLIB_EXPORT_CLASS(MyNodelet, nodelet::Nodelet)

Example

filter.yaml

output_queue_size: 100
scan_filter_chain:
  # Warning: using laser_filters like this might fail if they need to wait for some TFs; intensity filter does not.
  - name: intensity
    type: laser_filters/LaserScanIntensityFilter
    params:
      lower_threshold: 8000
      upper_threshold: 100000
      invert: false
      filter_override_range: true
      filter_override_intensity: false
  # This filter will subtract 25 ms from each scan timestamp, e.g. to account for transport delay.
  - name: delay
    type: sensor_filters/ChangeHeader/LaserScan
    params:
      stamp_relative: -0.025

filter.launch

<launch>
    <node pkg="sensor_filters" type="laserscan_filter_chain" output="screen" name="laser_filter">
        <remap from="~input" to="base_scan" />
        <remap from="~output" to="base_scan_filtered" />
        <rosparam command="load" file="$(dirname)/filter.yaml" />
    </node>
</launch>

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/ctu-vras/sensor_filters.git
VCS Type git
VCS Version master
Last Updated 2022-08-15
Dev Status DEVELOPED
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
sensor_filters 1.0.5

README

sensor_filters

This package is a collection of nodes and nodelets that service a filters::FilterChain for message types from sensor_msgs package. See https://wiki.ros.org/filters to read more about the filters package.

Attention: The PCL filters provided by package pcl_ros are not "compatible" with this package. They are instances of PCL filters, but written as ROS nodelets, not as ROS filters implementing the filters::FilterBase<> interface required by this package. Running pcl_ros filter nodelets does not require any additional setup except a running nodelet manager.

The task of each node(let) in sensor_filters is very simple: load the filter chain, subscribe ~input topic, and publish the filtered messages to ~output topic.

The size of message queues is configured via ~input_queue_size and ~output_queue_size, both defaulting to 10 messages.

The namespaces from which the filters load are the following:

Message Type Namespace Nodelet Name
CompressedImage ~image_filter_chain sensor_filters/compressedimage_filter_chain
Image ~image_filter_chain sensor_filters/image_filter_chain
Imu ~imu_filter_chain sensor_filters/imu_filter_chain
Joy ~joy_filter_chain sensor_filters/joy_filter_chain
LaserScan ~scan_filter_chain sensor_filters/laserscan_filter_chain
MagneticField ~magnetic_field_filter_chain sensor_filters/magneticfield_filter_chain
MultiEchoLaserScan ~scan_filter_chain sensor_filters/multiecholaserscan_filter_chain
NavSatFix ~nav_sat_fix_filter_chain sensor_filters/navsatfix_filter_chain
PointCloud ~cloud_filter_chain sensor_filters/pointcloud_filter_chain
PointCloud2 ~cloud_filter_chain sensor_filters/pointcloud2_filter_chain
Range ~range_filter_chain sensor_filters/range_filter_chain
RelativeHumidity ~humidity_filter_chain sensor_filters/relativehumidity_filter_chain
Temperature ~temperature_filter_chain sensor_filters/temperature_filter_chain

Provided filters

This package also provides a few high-level filters.

sensor_filters/ChangeHeader/$MESSAGE_TYPE

This filter allows changing the contents of the header of sensor messages. You can use it for example to correct the frame_id of a sensor, or adjust the timestamp of its messages.

Parameters

  • frame_id_prefix (string): Add this prefix to header.frame_id
  • frame_id_suffix (string): Add this suffix to header.frame_id
  • frame_id (string): Replace header.frame_idwith this value (prefix and suffix are then ignored)
  • seq (uint): Replace header.seq with this value
  • seq_relative (uint): Add this value to header.seq (watch out for unsigned int underflow)
  • stamp (double): Set header.stamp to this value (double value is converted to ros::Time)
  • stamp_relative (double): Add this value to header.stamp (double value is converted to ros::Duration)

Where to get other filters

If you are looking for other implementations of sensor filters, you will probably be disappointed, as they are really scarce. I don't know why it is the case, because filter chains are the most efficient data processing path ROS offers, yet most people chose to write nodelets instead.

There is, however, one noteworthy exception:

robot_body_filter

robot_body_filter is a versatile tool for removing the body parts of a robot from laser scans and point clouds exactly according to the robot's URDF model. No more box approximations of your robots! Represent them exactly as they are!

laser_filters compatibility

The LaserScan filter chain node is compatible with the scan_to_scan_node from laser_filters package (it can load the same filters using the same config). It does not, however, use TF message filter, so each filter has to wait for the required TFs itself.

Why ROS filters?

The simple answer is - performance. ROS filters are the most efficient way to run a chain of processors on sensor data. Passing the message from one filter to another is as simple as doing a C++ object copy. There is even a proposal for an in-place (zero-copy) implementation.

You can think about nodelets and their said performance. If used right and you run only non-modifying filters, you might actually get to true zero-copy filtering. But you have a dynamic allocation of at least one shared_ptr in your path, which might be costly and with unpredictable impacts on run time.

If you use nodelets and publish/subscribe references to messages instead of shared_ptrs, passing the message from one nodelet to another in the same manager not only does a copy of the message, but it also undergoes a serialization and deserialization step, which is most probably costly.

If you do not even use nodelets, but plain nodes, passing a message requires serializing it and sending via a local (or, even worse, remote TCP) socket to the other node. So you have copying, (de)serializing and sending via the network stack.

In the future, I plan to add some measurements to give some solid ground to these propositions.

Extensibility

Both the nodes and nodelets offer common code that can be shared so that you can build extensions of the simple filter chain handlers provided by this package.

See the examples folder for more verbose examples.

Nodes

#include <sensor_msgs/PointCloud2.h>
#include <sensor_filters/FilterChainNode.h>

class MyClass : public sensor_filters::FilterChainNode<sensor_msgs::PointCloud2>
{
  // custom class code
};

Nodelets

#include <pluginlib/class_list_macros.h>
#include <sensor_msgs/PointCloud2.h>
#include <sensor_filters/FilterChainNodelet.h>

class MyNodelet : public sensor_filters::FilterChainNodelet<sensor_msgs::PointCloud2>
{
  protected: void onInit() override
  {
    sensor_filters::FilterChainNodelet<sensor_msgs::PointCloud2>::onInit();
    // custom nodelet code
  }
};

PLUGINLIB_EXPORT_CLASS(MyNodelet, nodelet::Nodelet)

Example

filter.yaml

output_queue_size: 100
scan_filter_chain:
  # Warning: using laser_filters like this might fail if they need to wait for some TFs; intensity filter does not.
  - name: intensity
    type: laser_filters/LaserScanIntensityFilter
    params:
      lower_threshold: 8000
      upper_threshold: 100000
      invert: false
      filter_override_range: true
      filter_override_intensity: false
  # This filter will subtract 25 ms from each scan timestamp, e.g. to account for transport delay.
  - name: delay
    type: sensor_filters/ChangeHeader/LaserScan
    params:
      stamp_relative: -0.025

filter.launch

<launch>
    <node pkg="sensor_filters" type="laserscan_filter_chain" output="screen" name="laser_filter">
        <remap from="~input" to="base_scan" />
        <remap from="~output" to="base_scan_filtered" />
        <rosparam command="load" file="$(dirname)/filter.yaml" />
    </node>
</launch>

CONTRIBUTING

No CONTRIBUTING.md found.