Autonomous Navigation in Dynamic Environments using Sensor-Fusion based Multiple Object Tracking

J. Patel


To navigate safely through crowded dynamic environments, an autonomous system must analyze the behavior of surrounding objects and incorporate it into the actions taken. GET Lab currently employs a trajectory planner in conjunction with a reactive obstacle avoidance method. However, the motion of dynamic objects is not taken into consideration. To address this, Multiple Object Tracking (MOT) is employed in this thesis to interpret motion patterns of obstacles around the ego system. The tracking is performed based on the outcome from sensor fusion of camera and lidar data. The motion analysis is used to predict possible positions of the objects in the future. Potentially safe or unsafe areas in the robotís environment are modeled into a risk map by encoding the patterns and predictions. The risk map is integrated to the combination of a local and global path planner. This enables the robot to take early preventive measures while executing control commands. The developed system operates using a modular pipeline and is real-time capable. For evaluation, the system is tested on public datasets and simulated test scenarios.