Dispelling Illusions of Self-Motion: Robust Localisation for Autonomous Robots in Feature-Sparse and Dynamic Indoor Environments

Sammanfattning

Feature-sparse and dynamic environments pose significant challenges for autonomousrobots. This thesis presents an online method for removing dynamic objects from 2DLiDAR measurements. The dynamic filter first segments and tracks objects in thedata and uses a Kalman filter to estimate their speed and determine whether eachobject is static or dynamic. Testing shows that the dynamic filter improves localisationperformance in a feature-sparse and dynamic environment. Additionally, a nonholonomicCoordinated Turn (CT) model was implemented in the robot_localization(RL) library for ROS 2 to improve odometry pose estimation. Testing showed nonoticeable difference compared with RL’s default omnidirectional model.