Package Summary

Tags No category tags.
Version 3.0.1
License BSD
Build type CATKIN
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/AinsteinAI/ainstein_radar.git
VCS Type git
VCS Version master
Last Updated 2020-05-12
Dev Status MAINTAINED
CI status Continuous Integration : 0 / 0
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

Filtering and data conversion utilities for radar data.

Additional Links

Maintainers

  • Nick Rotella

Authors

  • Nick Rotella
README
No README found. See repository README.
CHANGELOG

Changelog for package ainstein_radar_filters

3.0.1 (2020-02-11)

  • Minor, add missing dependency
  • Contributors: Nick Rotella

3.0.0 (2020-02-06)

  • Add Cartesian tracking filter tracked pose output Added publishing of tracked object poses as geometry_msgs/PoseArray msg populated from tracked Cartesian position and using Cartesian velocity to determine pose. Forward direction (+x) is defined by tracked 3d velocity direction, with other axes determined by completing a right handed frame with +z up. Unfortunately, velocities cannot be published themselves without defining a custom TwistArray message, but nothing similar is supported natively in eg RViz.
  • Update Cartesian tracking filter, fix bugs and tune Updated the Cartesian tracking filter to use the measured position instead of measured unit direction, since we can measure range with most radars. Fixed other bugs in implementation and got working. Tuned resulting filter parameters to get decent tracking, now working similarly to old tracking filter (in spherical coordinates). Still need to add full Pose and Twist output; currently converting to RadarTarget for backwards-compatibility with other radar filters.
  • Minor, remove outdated filter config
  • Add Cartesian tracking filter, needs debugging Added a Cartesian coordinates tracking filter for radar data which stores object state as Cartesian position and velocity rather than spherical coordinate range/angles/speed. The advantage is that we can track the full Cartesian velocity by using information from the radar speed measurement (which is a projection onto the radian direction) and consecutive range/angle measurements. This unfortunately makes the KF into an EKF because the measurements are now nonlinear in Cartesian space. The filter compiles and runs but never outputs tracked targets in its current state, and thus needs debugging. Since the RadarTarget datatype is not capable of storing full 3d velocity anyway, we need to output eg Pose and Twist types instead. Since the filter stores uncertainty, we can use PoseWithCovarianceStamped and TwistWithCovarianceStamped.
  • Add support for point cloud input to radar tracking Added the ability for the radar tracking filter to subscribe to either a RadarTargetArray or PointCloud2 message. Internally, subscribing to a point cloud message converts to radar message type and then calls the original radar callback. This was added for portability since radar and point cloud messages are used with radar sensors often interchangeably.
  • Minor, explicitly copy header in conversion
  • Minor, remove outdated nodelet plugin
  • Merge branch \'master\' of https://github.com/AinsteinAI/ainstein_radar
  • Update combine filter example launch and fix bug Updated the radar combine filter example launch file in the ainstein_radar_filters package to show how to launch three K79 radars and run the combine filter. Also fixed a bug in which the dynamic configuration was set for all possible numbers of radars which caused runtime errors because only one of the time sync objects was valid. Now checking the number of topics and only setting that object.
  • Fix race condition in KF-based tracking filter Fixed a race condition in which the KF update loop and the radar data callback were in a race condition, with the number of KFs possibly changing during processing of new incoming data. Added a mutex to synchronize these functions, however the downside is potentially slower processing speed due to blocking waits for mutex unlocks in the data callback. Should be fine for small numbers of KFs.
  • Fix broken radar to point cloud conversion node
  • Minor, change passthrough filter default field
  • Add example launch for radar combine filter usage Added a launch file example for using the radar combine filter to sync three RadarTargetArray topics. Note that the topic names must be set as an arg and passed in through rosparam due to the parameter being a list of strings. Also note that the \"slop\" factor in message syncing has its default set in the cfg/ file and can be loaded using the usual dynamic reconfigure loader.
  • Add up to 8 synchronized topics in combine filter Added the ability to synchronize up to 8 RadarTargetArray topics in the combine filter. In theory the approximate time synchronizer in ROS can support 9 topics, HOWEVER boost::bind (which is used to register the callback) only supports 9 total arguments, the first of which in this case must be the \"this\" pointer because we bind a class member function here (see https://www.boost.org/doc/libs/1_45_0/libs/bind/bind.html#NumberOfArguments) The ability to synchronize radar topics beyond 3 has not been tested with data (only checked for compilation).
  • Merge branch \'master\' of https://github.com/AinsteinAI/ainstein_radar
  • Minor, add dynconfig update to combine filter
  • Minor, needed to negate speed for K79-3D using new SNR firmware
  • Refactor to clean up combine filter callbacks Refactored the combine filter\'s callbacks to use one central callback which works for any number of topics. This cleans up the overloaded callbacks a bit before adding the callbacks for >3 topics (to come next).
  • Minor, refactor combine topic names for consistency
  • Refactor combine filter for 2 or 3 topics via param Refactored the radar combine filter to approximately sync 2 or 3 topics by reading in a list of topic names and calling appropriate templated functions for setting up the time synchronizer and registering callback functions for each. This is not very clear but since the time sync variables are templated on the exact number of topics, the use of pre-defined templated variables for each number of topics seems unavoidable unfortunately. Tested by using rosbag_tools to change the frame ID of sample K79 data in different output bagfiles, playing back those bag files, remapping the topic name to something unique and defining static tf publishers to translate the data around before combining. Next, need to consolidate the actual combination callback code and add templated variables/functions for all other topic numbers (4-9, since ROS in C++ supports syncing up to 9 topics. Under the hood, this also uses messy templated code!)
  • Fix radar passthrough filter output transform Fixed the output radar passthrough filter cloud to transform the data properly in all cases.
  • Pull out coordinate transform utility functions Pulled utility functions for spherical<->Cartesian coordinate transforms and made message converstion depend on them.
  • Minor, add passthrough output tf to original frame
  • Fix radar passthrough filter output frame missing The output message published by the passthrough filter needs to be set explicitly, since the pcl conversions and passthrough doesn\'t preserve the frame ID (this should be debugged as it\'s desired for the frame to be set at each step). Thus, we explicitly set the output frame before publishing.
  • Add input/output frame params to radar passthrough Added the ability to specify input and output frames for the radar passthrough filter which function in the same manner as those in the pcl_ros package. The input_frame parameter specifies the frame in which the filtering should be performed, ie the input message is first converted to this frame before filtering. Note that this converts to a PointCloud2 and uses doTransform, so at the moment it does not transform the radar fields (range, angles) and is meant to be used for xyz filtering like the pcl_ros version. By default, the input frame is used and no transform is performed. The output_frame specifies the frame to publish the filtered data in after filtering. This is the original frame of the message by default but can be anything, including the input_frame. This functionality needs testing with hardware to confirm, and may be refactored to use a radar-specific doTransform soon.
  • Add PointCloud2 conversion to RadarTargetArray Added a ROS cloud to radar array conversion using the PCL to radar converter as an intermediate step.
  • Finish combine filter for two radar topics, tested Finished writing and testing the radar combine filter to approximately sync two RadarTargetArray topics and combine them via PointCloud2 combine functionality. This was tested with a single radar and the relay node from topic_tools package; next test with multiple radars.
  • Comment out radar transform function, add later Commented out the radar transform function because the pointcloud transform function from pcl_ros does not support tf2 in any way, so the best way to do this is probably convert to a PointCloud2 message and use tf2::doTransform. This function itself is templated on the data type and should be specialized to RadarTargetArray to do native tf2 transforming - add this later.
  • Fix pcl to radar conversion, add convert to degrees
  • Fix pcl to radar target conversion Fixed the pcl point to radar target conversion to calculate the new spherical coordinates instead of copying from the extra fiels in the PCL struct. This is necessary in case the point has been transformed before being converted back to a radar target.
  • Add combine filter for two radar topics, needs test Added a filter to combine two radar topics using an approximate time synchronizer, however this assumes the original messages are both in the same frame ID. Next, need to add conversion to the specified output frame ID. Compiles but needs testing.
  • Add changes to fix previous commit
  • Modify laser scan conversion to add nodelet Added a laser scan conversion nodelet after modifying the laser scan conversion class. Needs testing.
  • Minor, add missing nodelet to install
  • Remove range filter, deprecated by passthrough Removed the old radardata range filter because the new passthrough filter based on the PCL library passthrough filter functionally deprecates the range filter (passthrough is more general, as it applies to any field including range).
  • Refactor tracking and nearest target filter naming Refactored the tracking filter and nearest target filter to rename them according to a new convention for filters, removing the word radar since this is evident from the namespace ainstein_radar_filters scope.
  • Major refactor, add conversion header and nodelets Refactored the conversion utilities to live within a namespace instead of the radar to pointcloud class, changed their usage in all dependent files. Added nodelets for the passthrough and radar to pointcloud filters, tested on K79 data. Removed old nodelets which weren\'t being built properly.
  • Add generic passthrough filter for radar data Added a generic radar target array passthrough filter which functions exactly the same as those provided by pcl_ros. Uses dynamic reconfigure to allow changing the filter field and limits as well. Tested and works as expected. Next, will add a nodelet version.
  • Add conversions from point cloud back to radar data Added new conversions for PCL point/point cloud types to radar ROS message types (opposite of what previously existed).
  • Refactor radar to ROS point cloud conversion node Refactored the raadr to point cloud conversion node to separate the class (which only has static functions that should be moved to a utilities library at some point instead) from the node itself so that other classs/nodes can use the conversion functionality.
  • Minor fixes to radar+camera fusion launch and node Fixed the radar+camera fusion launch file to use the updated topic names for radar and camera data. Also fixed the fusion class itself to prevent crashing when empty bounding box arrays are processed. This node is still intended for use with the tracking filter.
  • Contributors: Nick Rotella

2.0.2 (2019-11-19)

  • Minor, fix header exports breaking bloom build
  • Rename input/output radar topics Renamed all instances of radardata_in and radardata_out to radar_in and radar_out to conform with other packages.
  • Fix laser scan converter params, remove deprecated Fixed the min/max range for the laserscan converted to be 0.0 and 100.0 respectively by default so that the filtering by range doesn\'t affect most radars by default. These paremeters are required by the laserscan message and should instead be set from the RadarInfo message, to be done soon. Also removed some deprecated code.
  • Remove deprecated file and code from pcl converter Removed an old file for testing the pointcloud (pcl) converter class and removed old code from the pointcloud converted class which was previously used to filter targets based on relative speed.
  • Contributors: Nick Rotella

2.0.1 (2019-11-12)

2.0.0 (2019-11-12)

  • Add K79 people tracking filter launch and params
  • Add tf2_eigen dependency to build
  • Minor, fix jsk messages dependency
  • Add bounding box output from radar tracking filter Added publication of bounding boxes for the tracked targets of the radar target tracking filter, computed to bound all targets used for a Kalman Filter update at each step. This is a sort of \"model-based clustering\" of radar data since the KF itself tracks with the aid of a simple motion model. Next, plan to add Cartesian pose+covariance output.
  • Contributors: Nick Rotella

1.1.0 (2019-10-29)

  • Minor, add radar SNR as laserscan intensities
  • Refactor pointcloud and laserscan converters Refactored the radar to pointcloud and laserscan conversion class and nodes in order to remove deprecated functionality and keep topic names consistent between them. The laserscan converter still has filtering based on the min/max angle/range parameters which should be removed and these parameters should be set from a radar sensor info message similar to camera info.
  • Minor fixes to package XML formatting Fixed the package XML file formatting and added missing content to conform to the suggested style guidelines.
  • Expose radar target array to point cloud conversion Exposed a function from ainstein_radar_filters which converts from the RadarTargetArray message type to the custom PCL point cloud type which includes radar data by making it static. This was needed for new tools which need easy access to a polar-to-cartesian function. It may make more sense to pull out such conversions and put them in utility class somewhere else. Note that the only change to CMakeLists required to expose the header from this package to any package which imports it was to add an INCLUDE_DIRS line in the catkin_Package() function of this package.
  • Add param for fixed frame to point cloud converter Add an optional fixed frame parameter for the radar target array to point cloud converted which takes in the name of the fixed frame, otherwise defaulting to map. Previously, map was hardcoded.
  • Refactor redundant radar to pointcloud class Refactored the old, redundant radar to pointcloud converter class and associated node/lets to a radar speed filter class, preserving the projected speed filtering functionality. Tested on K79 data and working with mock zero speed command, should test further with nonzero GPS speed. There is still deprecated functionality in this class for testing the radar rotated which should be removed at some point as this was only experimental.
  • Use custom PCL radar point for data converter class Switched from using the normal pcl::PointXYZ type to the custom radar specific ainstein_radar_filters::PointRadarTarget type in the radar to point cloud conversion class. Tested on radar data and verified that it allows coloring clouds according to additional radar-specific fields eg range, speed, etc. This permits using existing point cloud-based filter node/lets to filter based on radar parameters, deprecating eg the range filter class in this repo. Also removed a debug printout from the rviz plugin class.
  • Contributors: Nick Rotella

1.0.3 (2019-10-03)

  • Minor bug fix in tracking filter update Fixed a small bug in the Kalman Filter state covariance update equation which had an extra transpose in it. This likely didn\'t affect filter performance noticeably because it only affected off-diagonal elements.
  • Contributors: Nick Rotella

1.0.2 (2019-09-25)

1.0.1 (2019-09-24)

  • Refactor filters into separate subpkg, fix bug Created subpackage ainstein_radar_filters for radar filters and conversions, moved all filters from ainstein_radar_drivers into this subpkg and tested build and launch on rosbag data. Also fixed a small bug in the radar data range filter in which the dynamic reconfigure callback was not being registered, preventing the filter from working. Now, the filter compiles and works properly.
  • Contributors: Nick Rotella

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Launch files

Messages

No message files found.

Services

No service files found

Recent questions tagged ainstein_radar_filters at answers.ros.org

Package Summary

Tags No category tags.
Version 3.0.1
License BSD
Build type CATKIN
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/AinsteinAI/ainstein_radar.git
VCS Type git
VCS Version master
Last Updated 2020-05-12
Dev Status MAINTAINED
CI status Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

Filtering and data conversion utilities for radar data.

Additional Links

Maintainers

  • Nick Rotella

Authors

  • Nick Rotella
README
No README found. See repository README.
CHANGELOG

Changelog for package ainstein_radar_filters

3.0.1 (2020-02-11)

  • Minor, add missing dependency
  • Contributors: Nick Rotella

3.0.0 (2020-02-06)

  • Add Cartesian tracking filter tracked pose output Added publishing of tracked object poses as geometry_msgs/PoseArray msg populated from tracked Cartesian position and using Cartesian velocity to determine pose. Forward direction (+x) is defined by tracked 3d velocity direction, with other axes determined by completing a right handed frame with +z up. Unfortunately, velocities cannot be published themselves without defining a custom TwistArray message, but nothing similar is supported natively in eg RViz.
  • Update Cartesian tracking filter, fix bugs and tune Updated the Cartesian tracking filter to use the measured position instead of measured unit direction, since we can measure range with most radars. Fixed other bugs in implementation and got working. Tuned resulting filter parameters to get decent tracking, now working similarly to old tracking filter (in spherical coordinates). Still need to add full Pose and Twist output; currently converting to RadarTarget for backwards-compatibility with other radar filters.
  • Minor, remove outdated filter config
  • Add Cartesian tracking filter, needs debugging Added a Cartesian coordinates tracking filter for radar data which stores object state as Cartesian position and velocity rather than spherical coordinate range/angles/speed. The advantage is that we can track the full Cartesian velocity by using information from the radar speed measurement (which is a projection onto the radian direction) and consecutive range/angle measurements. This unfortunately makes the KF into an EKF because the measurements are now nonlinear in Cartesian space. The filter compiles and runs but never outputs tracked targets in its current state, and thus needs debugging. Since the RadarTarget datatype is not capable of storing full 3d velocity anyway, we need to output eg Pose and Twist types instead. Since the filter stores uncertainty, we can use PoseWithCovarianceStamped and TwistWithCovarianceStamped.
  • Add support for point cloud input to radar tracking Added the ability for the radar tracking filter to subscribe to either a RadarTargetArray or PointCloud2 message. Internally, subscribing to a point cloud message converts to radar message type and then calls the original radar callback. This was added for portability since radar and point cloud messages are used with radar sensors often interchangeably.
  • Minor, explicitly copy header in conversion
  • Minor, remove outdated nodelet plugin
  • Merge branch \'master\' of https://github.com/AinsteinAI/ainstein_radar
  • Update combine filter example launch and fix bug Updated the radar combine filter example launch file in the ainstein_radar_filters package to show how to launch three K79 radars and run the combine filter. Also fixed a bug in which the dynamic configuration was set for all possible numbers of radars which caused runtime errors because only one of the time sync objects was valid. Now checking the number of topics and only setting that object.
  • Fix race condition in KF-based tracking filter Fixed a race condition in which the KF update loop and the radar data callback were in a race condition, with the number of KFs possibly changing during processing of new incoming data. Added a mutex to synchronize these functions, however the downside is potentially slower processing speed due to blocking waits for mutex unlocks in the data callback. Should be fine for small numbers of KFs.
  • Fix broken radar to point cloud conversion node
  • Minor, change passthrough filter default field
  • Add example launch for radar combine filter usage Added a launch file example for using the radar combine filter to sync three RadarTargetArray topics. Note that the topic names must be set as an arg and passed in through rosparam due to the parameter being a list of strings. Also note that the \"slop\" factor in message syncing has its default set in the cfg/ file and can be loaded using the usual dynamic reconfigure loader.
  • Add up to 8 synchronized topics in combine filter Added the ability to synchronize up to 8 RadarTargetArray topics in the combine filter. In theory the approximate time synchronizer in ROS can support 9 topics, HOWEVER boost::bind (which is used to register the callback) only supports 9 total arguments, the first of which in this case must be the \"this\" pointer because we bind a class member function here (see https://www.boost.org/doc/libs/1_45_0/libs/bind/bind.html#NumberOfArguments) The ability to synchronize radar topics beyond 3 has not been tested with data (only checked for compilation).
  • Merge branch \'master\' of https://github.com/AinsteinAI/ainstein_radar
  • Minor, add dynconfig update to combine filter
  • Minor, needed to negate speed for K79-3D using new SNR firmware
  • Refactor to clean up combine filter callbacks Refactored the combine filter\'s callbacks to use one central callback which works for any number of topics. This cleans up the overloaded callbacks a bit before adding the callbacks for >3 topics (to come next).
  • Minor, refactor combine topic names for consistency
  • Refactor combine filter for 2 or 3 topics via param Refactored the radar combine filter to approximately sync 2 or 3 topics by reading in a list of topic names and calling appropriate templated functions for setting up the time synchronizer and registering callback functions for each. This is not very clear but since the time sync variables are templated on the exact number of topics, the use of pre-defined templated variables for each number of topics seems unavoidable unfortunately. Tested by using rosbag_tools to change the frame ID of sample K79 data in different output bagfiles, playing back those bag files, remapping the topic name to something unique and defining static tf publishers to translate the data around before combining. Next, need to consolidate the actual combination callback code and add templated variables/functions for all other topic numbers (4-9, since ROS in C++ supports syncing up to 9 topics. Under the hood, this also uses messy templated code!)
  • Fix radar passthrough filter output transform Fixed the output radar passthrough filter cloud to transform the data properly in all cases.
  • Pull out coordinate transform utility functions Pulled utility functions for spherical<->Cartesian coordinate transforms and made message converstion depend on them.
  • Minor, add passthrough output tf to original frame
  • Fix radar passthrough filter output frame missing The output message published by the passthrough filter needs to be set explicitly, since the pcl conversions and passthrough doesn\'t preserve the frame ID (this should be debugged as it\'s desired for the frame to be set at each step). Thus, we explicitly set the output frame before publishing.
  • Add input/output frame params to radar passthrough Added the ability to specify input and output frames for the radar passthrough filter which function in the same manner as those in the pcl_ros package. The input_frame parameter specifies the frame in which the filtering should be performed, ie the input message is first converted to this frame before filtering. Note that this converts to a PointCloud2 and uses doTransform, so at the moment it does not transform the radar fields (range, angles) and is meant to be used for xyz filtering like the pcl_ros version. By default, the input frame is used and no transform is performed. The output_frame specifies the frame to publish the filtered data in after filtering. This is the original frame of the message by default but can be anything, including the input_frame. This functionality needs testing with hardware to confirm, and may be refactored to use a radar-specific doTransform soon.
  • Add PointCloud2 conversion to RadarTargetArray Added a ROS cloud to radar array conversion using the PCL to radar converter as an intermediate step.
  • Finish combine filter for two radar topics, tested Finished writing and testing the radar combine filter to approximately sync two RadarTargetArray topics and combine them via PointCloud2 combine functionality. This was tested with a single radar and the relay node from topic_tools package; next test with multiple radars.
  • Comment out radar transform function, add later Commented out the radar transform function because the pointcloud transform function from pcl_ros does not support tf2 in any way, so the best way to do this is probably convert to a PointCloud2 message and use tf2::doTransform. This function itself is templated on the data type and should be specialized to RadarTargetArray to do native tf2 transforming - add this later.
  • Fix pcl to radar conversion, add convert to degrees
  • Fix pcl to radar target conversion Fixed the pcl point to radar target conversion to calculate the new spherical coordinates instead of copying from the extra fiels in the PCL struct. This is necessary in case the point has been transformed before being converted back to a radar target.
  • Add combine filter for two radar topics, needs test Added a filter to combine two radar topics using an approximate time synchronizer, however this assumes the original messages are both in the same frame ID. Next, need to add conversion to the specified output frame ID. Compiles but needs testing.
  • Add changes to fix previous commit
  • Modify laser scan conversion to add nodelet Added a laser scan conversion nodelet after modifying the laser scan conversion class. Needs testing.
  • Minor, add missing nodelet to install
  • Remove range filter, deprecated by passthrough Removed the old radardata range filter because the new passthrough filter based on the PCL library passthrough filter functionally deprecates the range filter (passthrough is more general, as it applies to any field including range).
  • Refactor tracking and nearest target filter naming Refactored the tracking filter and nearest target filter to rename them according to a new convention for filters, removing the word radar since this is evident from the namespace ainstein_radar_filters scope.
  • Major refactor, add conversion header and nodelets Refactored the conversion utilities to live within a namespace instead of the radar to pointcloud class, changed their usage in all dependent files. Added nodelets for the passthrough and radar to pointcloud filters, tested on K79 data. Removed old nodelets which weren\'t being built properly.
  • Add generic passthrough filter for radar data Added a generic radar target array passthrough filter which functions exactly the same as those provided by pcl_ros. Uses dynamic reconfigure to allow changing the filter field and limits as well. Tested and works as expected. Next, will add a nodelet version.
  • Add conversions from point cloud back to radar data Added new conversions for PCL point/point cloud types to radar ROS message types (opposite of what previously existed).
  • Refactor radar to ROS point cloud conversion node Refactored the raadr to point cloud conversion node to separate the class (which only has static functions that should be moved to a utilities library at some point instead) from the node itself so that other classs/nodes can use the conversion functionality.
  • Minor fixes to radar+camera fusion launch and node Fixed the radar+camera fusion launch file to use the updated topic names for radar and camera data. Also fixed the fusion class itself to prevent crashing when empty bounding box arrays are processed. This node is still intended for use with the tracking filter.
  • Contributors: Nick Rotella

2.0.2 (2019-11-19)

  • Minor, fix header exports breaking bloom build
  • Rename input/output radar topics Renamed all instances of radardata_in and radardata_out to radar_in and radar_out to conform with other packages.
  • Fix laser scan converter params, remove deprecated Fixed the min/max range for the laserscan converted to be 0.0 and 100.0 respectively by default so that the filtering by range doesn\'t affect most radars by default. These paremeters are required by the laserscan message and should instead be set from the RadarInfo message, to be done soon. Also removed some deprecated code.
  • Remove deprecated file and code from pcl converter Removed an old file for testing the pointcloud (pcl) converter class and removed old code from the pointcloud converted class which was previously used to filter targets based on relative speed.
  • Contributors: Nick Rotella

2.0.1 (2019-11-12)

2.0.0 (2019-11-12)

  • Add K79 people tracking filter launch and params
  • Add tf2_eigen dependency to build
  • Minor, fix jsk messages dependency
  • Add bounding box output from radar tracking filter Added publication of bounding boxes for the tracked targets of the radar target tracking filter, computed to bound all targets used for a Kalman Filter update at each step. This is a sort of \"model-based clustering\" of radar data since the KF itself tracks with the aid of a simple motion model. Next, plan to add Cartesian pose+covariance output.
  • Contributors: Nick Rotella

1.1.0 (2019-10-29)

  • Minor, add radar SNR as laserscan intensities
  • Refactor pointcloud and laserscan converters Refactored the radar to pointcloud and laserscan conversion class and nodes in order to remove deprecated functionality and keep topic names consistent between them. The laserscan converter still has filtering based on the min/max angle/range parameters which should be removed and these parameters should be set from a radar sensor info message similar to camera info.
  • Minor fixes to package XML formatting Fixed the package XML file formatting and added missing content to conform to the suggested style guidelines.
  • Expose radar target array to point cloud conversion Exposed a function from ainstein_radar_filters which converts from the RadarTargetArray message type to the custom PCL point cloud type which includes radar data by making it static. This was needed for new tools which need easy access to a polar-to-cartesian function. It may make more sense to pull out such conversions and put them in utility class somewhere else. Note that the only change to CMakeLists required to expose the header from this package to any package which imports it was to add an INCLUDE_DIRS line in the catkin_Package() function of this package.
  • Add param for fixed frame to point cloud converter Add an optional fixed frame parameter for the radar target array to point cloud converted which takes in the name of the fixed frame, otherwise defaulting to map. Previously, map was hardcoded.
  • Refactor redundant radar to pointcloud class Refactored the old, redundant radar to pointcloud converter class and associated node/lets to a radar speed filter class, preserving the projected speed filtering functionality. Tested on K79 data and working with mock zero speed command, should test further with nonzero GPS speed. There is still deprecated functionality in this class for testing the radar rotated which should be removed at some point as this was only experimental.
  • Use custom PCL radar point for data converter class Switched from using the normal pcl::PointXYZ type to the custom radar specific ainstein_radar_filters::PointRadarTarget type in the radar to point cloud conversion class. Tested on radar data and verified that it allows coloring clouds according to additional radar-specific fields eg range, speed, etc. This permits using existing point cloud-based filter node/lets to filter based on radar parameters, deprecating eg the range filter class in this repo. Also removed a debug printout from the rviz plugin class.
  • Contributors: Nick Rotella

1.0.3 (2019-10-03)

  • Minor bug fix in tracking filter update Fixed a small bug in the Kalman Filter state covariance update equation which had an extra transpose in it. This likely didn\'t affect filter performance noticeably because it only affected off-diagonal elements.
  • Contributors: Nick Rotella

1.0.2 (2019-09-25)

1.0.1 (2019-09-24)

  • Refactor filters into separate subpkg, fix bug Created subpackage ainstein_radar_filters for radar filters and conversions, moved all filters from ainstein_radar_drivers into this subpkg and tested build and launch on rosbag data. Also fixed a small bug in the radar data range filter in which the dynamic reconfigure callback was not being registered, preventing the filter from working. Now, the filter compiles and works properly.
  • Contributors: Nick Rotella

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Launch files

Messages

No message files found.

Services

No service files found

Recent questions tagged ainstein_radar_filters at answers.ros.org