amcl: ros__parameters: # Odometry motion model type. robot_model_type: hmc_amcl::DifferentialMotionModel # Expected process noise in odometry’s rotation estimate from rotation. alpha1: 0.05 # Expected process noise in odometry’s rotation estimate from translation. alpha2: 0.05 # Expected process noise in odometry’s translation estimate from translation. alpha3: 0.1 # Expected process noise in odometry’s translation estimate from rotation. alpha4: 0.1 # Expected process noise in odometry's strafe estimate from translation. alpha5: 0.1 # The name of the coordinate frame published by the localization system. global_frame_id: map # The name of the coordinate frame published by the odometry system. odom_frame_id: odom # The name of the coordinate frame of the robot base. base_frame_id: base_link # The name of the topic where the map is published by the map server. map_topic: map_nav # The name of the topic where scans are being published. scan_topic: /scan # The name of the topic where an initial pose can be published. # The particle filter will be reset using the provided pose with covariance. initial_pose_topic: initialpose # Maximum number of particles that will be used. max_particles: 5000 # Minimum number of particles that will be used. min_particles: 1000 # Error allowed by KLD criteria. pf_err: 0.05 # KLD criteria parameter. # Upper standard normal quantile for the probability that the error in the # estimated distribution is less than pf_err. pf_z: 0.99 # Fast exponential filter constant, used to filter the average particles weights. # Random particles are added if the fast filter result drops below the slow filter result, # allowing the particle filter to recover from a bad approximation. Keep disabled for # stable tracking unless kidnapped-robot recovery is required. recovery_alpha_fast: 0.0 # Slow exponential filter constant, used to filter the average particles weights. # Random particles are added if the fast filter result drops below the slow filter result, # allowing the particle filter to recover from a bad approximation. Keep disabled for # stable tracking unless kidnapped-robot recovery is required. recovery_alpha_slow: 0.0 # Resample will happen after the amount of updates specified here happen. resample_interval: 1 # Minimum angle difference from last resample for resampling to happen again. update_min_a: 0.05 # Maximum angle difference from last resample for resampling to happen again. update_min_d: 0.05 # Laser sensor model type. laser_model_type: likelihood_field # Maximum distance of an obstacle (if the distance is higher, this one will be used in the likelihood map). laser_likelihood_max_dist: 2.0 # Maximum range of the laser. laser_max_range: 30.0 # Maximum number of beams to use in the likelihood field sensor model. max_beams: 200 # Weight used to combine the probability of hitting an obstacle. z_hit: 0.9 # Weight used to combine the probability of random noise in perception. z_rand: 0.1 # Weight used to combine the probability of getting short readings. z_short: 0.05 # Weight used to combine the probability of getting max range readings. z_max: 0.05 # Standard deviation of a gaussian centered around obstacles. sigma_hit: 0.05 #0.2 # Whether to broadcast map to odom transform or not. tf_broadcast: true # Transform tolerance allowed. transform_tolerance: 0.5 #1.0 # Execution policy used to apply the motion update and importance weight steps. # Valid options: "seq", "par". execution_policy: seq # Set this to true when you want to load only the first published map from map_server and ignore subsequent ones. first_map_only: false # Whether to set initial pose based on parameters. # When enabled, particles will be initialized with the specified pose coordinates and covariance. set_initial_pose: false # Maximum rate in Hz at which the latest pose is saved to saved_pose_filepath. # Set to 0.0 or a negative value to disable pose persistence. save_pose_rate: 0.5 # Whether to initialize from the pose stored in saved_pose_filepath when set_initial_pose is false. initialize_at_saved_pose: true # File used to persist and restore the latest AMCL pose. saved_pose_filepath: /tmp/amcl_saved_pose # If false, AMCL will use the last known pose to initialize when a new map is received. always_reset_initial_pose: false # Use the latest laser scan to score global-localization candidates before seeding AMCL. global_localization_scan_matching: true # Maximum number of beams used only for scan-matched global localization. global_localization_max_beams: 200 # Coarse XY spacing, in meters, for scan-matched global localization search. global_localization_coarse_xy_step: 0.5 # Number of coarse yaw bins for scan-matched global localization search. global_localization_yaw_bins: 36 # Number of scan-matched global localization hypotheses to seed into AMCL. global_localization_max_candidates: 8 # XY radius, in meters, used to refine coarse global localization candidates. global_localization_refine_xy_radius: 0.25 # XY step, in meters, used to refine coarse global localization candidates. global_localization_refine_xy_step: 0.1 # Yaw step, in radians, used to refine coarse global localization candidates. global_localization_refine_yaw_step: 0.08726646259971647 # Uniform XY noise radius, in meters, used when sampling around global candidates. global_localization_xy_noise: 0.2 # Uniform yaw noise radius, in radians, used when sampling around global candidates. global_localization_yaw_noise: 0.17453292519943295 # Initial pose x coordinate. initial_pose.x: 0.0 # Initial pose y coordinate. initial_pose.y: -2.0 # Initial pose yaw coordinate. initial_pose.yaw: 0.0 # Initial pose xx covariance. initial_pose.covariance_x: 0.25 # Initial pose yy covariance. initial_pose.covariance_y: 0.25 # Initial pose yawyaw covariance. initial_pose.covariance_yaw: 0.0685 # Initial pose xy covariance. initial_pose.covariance_xy: 0.0 # Initial pose xyaw covariance. initial_pose.covariance_xyaw: 0.0 # Initial pose yyaw covariance. initial_pose.covariance_yyaw: 0.0