Authors: Elisa Stefanini, Enrico Ciancolini

lidar_based_map_updating

Ros-Packages to update an occupancy grid map with lidar measurements.

Abstract

Long-time operations of autonomous vehicles and mobile robots in logistics and service applications are still a challenge. To avoid a continuous re-mapping, the map can be updated to obtain a consistent representation of the current environment. In this paper, we propose a novel LIDAR-based occupancy grid map updating algorithm for dynamic environments taking into account possible localisation and measurement errors. The proposed approach allows robust long-term operations as it can detect changes in the working area even in presence of moving elements. Results highlighting \modified{map} quality and localisation performance, both in simulation and experiments, are reported.

Requirements:

• A localisation algorithm
• An occupancy grid map

Parameters present in parameters.yaml file:

• changed_cells_topic: name of the changed cells topic

• map_topic: name of the map topic

• processed_map_topic: /processed_map name of the updated map topic

• map_updater:

  • base_frame: name of the robot base frame name

  • map_frame: name of the map frame

  • scan_topic: name of the scan topic

  • odom_topic: name of the odom topic

  • update_map_online: true

  • init_buffers: false

  • min_angle_increment: 0.1 #0.5 # expressed in degrees, it determines the considered beams number

  The pairing distance is taken as a function of the measured range R. In particular, it grows linearly with R until saturation is reached The configurable parameters are: saturation value and the desired pairing distance for two different range measurement

  • range_1_pair: 0.2

  • pairing_distance_1: 0.15

  • range_2_pair: 10.0

  • pairing_distance_2: 0.28

  • pairing_distance_saturation: 0.3

  • buffer_size: 10 rolling buffer size

  • counters_change_threshold: 7 cell is considered as changed if the corresponding counter is greater than this threshold

  • counters_change_hysteresis: 0

  • scan_update_interval: 0.3 min time that has to elapse between two scans processing

  • min_linear_displacement: 0.4 min distance that the robot has to travel between two scans processing

  • min_angular_displacement: 45.0 expressed in degrees

  • max_beam_range: 18.0 measurements greater than this will be discarded

  • max_range_tol: 0.8 virtual beams will measure no more than "max_beam_range" + "max_range_tol"

  • virtual_to_measured_beam_ratio: 1 determines the number of virtual beams

  • min_search_window_halfwidth_degrees: 4.0 expressed in degrees

  • max_trusted_angular_velocity: 12 expressed in degrees/s

  • min_idle_duration: 1.2 min idle state time induced by a quick rotation

  • min_idle_distance: 1.6

  • final_chunk_skip_fraction: 0.02 final fraction of the beam that is skipped when doing ray casting for anomalous beams analysis

  • min_chunk_skip_length: 0.15 min skipped length

  • skip_fraction_false_positive: 0.02 final fraction of the beam that is skipped when doing ray casting for false positive detection

  • N_skip_anomalous: 2 number of not anomalous beams that has to be skipped near an anomalous beam

  • max_anomalous_beam_fraction: 0.8

  For each anomalous point, a search for an occupied cell is performed within a circle centered in the hit point and with radius that grows linearly with the measured range

  • range_1_search: 0.0
  • radius_1: 0.10
  • range_2_search: 10.0
  • radius_2: 0.2

Files in Launch folder:

• start_map_update.launch --> launch updating node
• monitor_consumption.launch --> launch cpu_monitor node
• update_bag --> launch updating node for bag file

Input topic for the map updating algoritm:

• scan topic
• map topic
• odom topic

Output topic:

• updated map --> /processed_map
• changed_cells_topic --> /changed_cells