3D Objects Detection and Recognition from Color and LiDAR Data for Autonomous Driving
Lian Kang, Pierre Payeur · EPiC series in computing · 2023
In recent years, autonomous driving vehicles are attracting growing commercial and scientific attention. How to detect and recognize objects in a complex real-world road environment represents one of the most important problems facing autonomous vehicles and their ability to make decisions on the road and in real time. While color imaging remains a rich source of information, LiDAR scanners can collect high quality data under different lighting conditions and can provide high-range and high-precision spatial information. Expanding object detection by processing simultaneously data collected by a color camera and a LiDAR scanner brings new capabilities to the field of autonomous driving. In this paper, a 3D object detector is proposed with focal loss and Euler angle regression to optimize the detector’s performance. It uses a bird’s-eye view map generated from a LiDAR point cloud and RGB images as input. Results show that the proposed 3D object detector reaches a speed over 46 frames per second and an average precision over 90%. In addition, a more compact detector is also proposed that processes the same input data three times faster with only slightly lower accuracy.