360$$^{\circ }$$ Real-Time 3D Multi-object Detection and Tracking for Autonomous Vehicle Navigation

This paper presents a real-time 3D Multi Object Detection and Tracking (DAMOT) method proposed for the UAH autonomous electric car. It allows the vehicle to recognize 360 surrounding objects and uniquely identify them to be able to follow their trajectory in scene by only receiving a 3D point cloud through ROS framework. First, we describe our proposal of 3D object detector, based on PointPillars [11], processing LiDAR data to locate objects in space obtaining their dimensions and location. Secondly, we use BEV-MOT [7], our Multi-Object Tracking technique in order to uniquely identify each object over a Bird’s-Eye View (BEV) through a combination of 2D Kalman filter and Hungarian algorithm, allowing the ego-vehicle to follow surrounding objects trajectories. A comparison of the performance of our proposal with other state-of-the-art methods is carried out applying KITTI-3DMOT evaluation tool extracted from AB3DMOT [21] on KITTI [5] validation dataset. Finally, we validate our DAMOT in several traffic scenarios implemented in CARLA [4] open-source driving simulator by using AB4COGT tool, designed by authors, studying its performance in a controlled but realistic urban environment with real-time execution, providing several demonstration videos (https://cutt.ly/3rU113d).