README

Beluga AMCL

Beluga AMCL is a ROS node based on the Beluga library that aims to be fully compatible with the Navigation 2 AMCL node.
The compatibility between beluga_amcl and its longstanding counterparts in the ROS ecosystem provides a simple migration path for projects that want to be able to integrate the power and modularity of the Beluga library in an existing nav2_amcl-based project.

Table of Contents

• Beluga AMCL

  • Table of Contents

  • ROS Interface

    • Parameters

    • Subscribed Topics

    • Published Topics

    • Published Transforms

    • Advertised Services

  • Performance

  • Next Steps

ROS Interface

Parameters

Beluga AMCL currently supports the majority of ROS parameters used in Navigation 2 AMCL.
See Beluga AMCL documentation for further reference.

Subscribed Topics

The subscribed topic names can be changed with the parameters map_topic, scan_topic, and initial_pose_topic.

Topic	Type	Description
map	nav_msgs/OccupancyGrid	Input topic for map updates.
scan	sensor_msgs/LaserScan	Input topic for laser scan updates.
initial_pose	geometry_msgs/PoseWithCovarianceStamped	Input topic for pose mean and covariance to initialize the particle filter.

Alternatively, and instead of the scan_topic, one may set the point_cloud_topic. Point clouds are assumed to be contained in a z = constant plane in the base frame of reference. It is further assumed this plane is the same plane where the map and pose estimates are defined. If this is not the case, Beluga AMCL will misbehave. It is on the user to filter point clouds and make sure these assumption hold.

Published Topics

Topic	Type	Description
particle_cloud	geometry_msgs/PoseArray	Output topic for particle cloud poses published at a fixed frequency.
particle_markers	visualization_msgs/MarkerArray	Output topic for particle cloud markers published at a fixed frequency.
likelihood_field	nav_msgs/OccupancyGrid	Output topic for likelihood field, published on update when applicable.
pose	geometry_msgs/PoseWithCovarianceStamped	Output topic for estimated pose mean and covariance in map frame.

Published Transforms

The frame names can be changed with the parameters global_frame_id, odom_frame_id and base_frame_id. Defaults are map, odom and base.

Transform	Description
odom to base	Input transform used by motion models and resampling policies.
base to laser	Input transform used to convert laser scan points to base frame.
map to odom	Output transform calculated from the estimated pose mean and the current odom-to-base transform.

Advertised Services

Topic	Type	Description
reinitialize_global_localization	std_srvs/Empty	Request to reinitialize global localization without an initial pose estimate.
request_nomotion_update	std_srvs/Empty	Trigger a forced update of the filter estimates.

Performance

Performance reports are periodically generated and uploaded to track performance improvements and regressions. These reports are generated using a set of scripts in the beluga_benchmark package which can be used to compare the performance of beluga_amcl against that of nav2_amcl using a synthetic dataset.

The following plot displays the RSS (Resident Set Size), CPU usage, APE (Absolute Pose Error) and processing latency statistics for both beluga_amcl and nav2_amcl, with particle sizes ranging between 250 and 200000 and sensor model likelihood field.

The following plot displays the RSS (Resident Set Size), CPU usage, APE (Absolute Pose Error) and processing latency statistics for both beluga_amcl and nav2_amcl, with particle sizes ranging between 250 and 200000 and sensor model beam.

Further details can be found in the reports folder here.

Next Steps

• See example launch files showing how to run Beluga-based nodes.

• See available benchmarks for scripts and comparison with other AMCL implementations.