amcl calls this service to retrieve the map that is used for laser-based localization startup blocks on getting the map from this service.Services Called static_map ( nav_msgs/GetMap) Service to manually set a new map and pose.Service to manually perform update and publish updated particles.Request_nomotion_update ( std_srvs/Empty) Initiate global localization, wherein all particles are dispersed randomly through the free space in the map.Services global_localization ( std_srvs/Empty) Publishes the transform from odom (which can be remapped via the ~odom_frame_id parameter) to map.The set of pose estimates being maintained by the filter.Robot's estimated pose in the map, with covariance.Published Topics amcl_pose ( geometry_msgs/PoseWithCovarianceStamped) When the use_map_topic parameter is set, AMCL subscribes to this topic to retrieve the map used for laser-based localization.Mean and covariance with which to (re-)initialize the particle filter. Subscribed Topics scan ( sensor_msgs/LaserScan) Note that, because of the defaults, if no parameters are set, the initial filter state will be a moderately sized particle cloud centered about (0,0,0). On startup, amcl initializes its particle filter according to the parameters provided. To localize using laser data on the base_scan topic: amcl scan:=base_scanĪmcl amcl takes in a laser-based map, laser scans, and transform messages, and outputs pose estimates. It could be extended to work with other sensor data. In particular, we use the following algorithms from that book: sample_motion_model_odometry, beam_range_finder_model, likelihood_field_range_finder_model, Augmented_MCL, and KLD_Sampling_MCL.Īs currently implemented, this node works only with laser scans and laser maps. The user is advised to check there for more detail. Many of the algorithms and their parameters are well-described in the book Probabilistic Robotics, by Thrun, Burgard, and Fox.
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