3D Objects Detection and Recognition from Color and LiDAR Data for Autonomous Driving

14 pages•Published: March 22, 2023

Abstract

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.

Keyphrases: autonomous driving, deep learning, lidar sensing, object detection

In: Ajay Bandi, Mohammad Hossain and Ying Jin (editors). Proceedings of 38th International Conference on Computers and Their Applications, vol 91, pages 42-55.

Links:	https://easychair.org/publications/paper/KMBH
	https://doi.org/10.29007/bbg7