Growing neural gas based 3D space object tracking for autonomous mobile robot

In order for autonomous mobile robots to play an active role in our daily lives, the robots must be able to recognize an unknown 3D environment in real time. To address this challenge, we introduce an innovative method for object tracking using 3D point clouds as input. Our approach leverages the Growing Neural Gas (GNG) algorithm for real-time environment recognition in unfamiliar settings, coupled with object detection using Axis-Aligned Bounding Box (AABB), and Intersection over Union (IoU) for precise tracking. Our method achieves real-time object tracking in an unknown environment, processing in approximately 33 milliseconds. The proposed method showcases performance with an average IoU value of around 0.77, which was sufficient for object tracking. Finally, we validate the efficacy of this method by conducting two experiments in both a simulated environment using a depth camera and a real environment using a 3D-Lidar.