My current setup involves a single LiDAR sensor that covers only a limited 220-degree arc of the robot's surroundings. As the robot moves, any obstacles seen by the LiDAR previously and not in the LiDAR coverage remain on the costmap since the costmap can not raytraces them, as there are no laser beams that can be traced in that region. This creates a challenge as the costmap, responsible for guiding the robot's path planning, cannot effectively update and clear regions where the LiDAR doesn't provide data. Consequently, the costmap becomes cluttered with numerous false obstacles, significantly affecting the map's accuracy and usability.
What would be the best technique to tackle this?
Actually, I have identified the source of the issue. It is not originating from the costmap; instead, it is stemming from the AMCL (Adaptive Monte Carlo Localization) module. The robot is encountering difficulties in localizing itself accurately, particularly during rotational movements. This problem arises because the laser scan data moves along with the robot, rather than staying aligned with the static map. As a result, the costmap is becoming distorted, leading to inaccurate localization.
The current question revolves around whether a 220-degree field of view for the laser scan will provide sufficient features for AMCL to achieve proper robot localization. I am confident that the problem does not stem from the odometry, wheel separation, or wheel radius, as I have conducted tests using the same setup and AMCL parameters with two lidars providing a 360-degree view, yielding impeccable results.