Automotive LiDAR System and Its Multi-Sensor Fusion Denoising

Abstract

With the rapid development of intelligent connected vehicles and autonomous driving technology in recent years, environmental perception has gradually been added to ensure the safety and reliability of autonomous driving; now, automotive LiDAR is one of the required sensors in the perception system because it has high-precision 3D space detection and strong anti-interference ability. The point cloud data obtained by LiDAR is often affected by internal hardware faults and various complicated external environmental factors; therefore, there is a lot of noise that can damage the operation of autonomous driving perception. Research on the Development of Automotive LiDAR Systems and Denoising Technology for Multi-sensor Fusion. It systematically introduces the working principle and range mechanism of ToF and FMCW, the technical development path of automotive LiDAR, typical point cloud noise sources, relevant denoising methods, etc. At the same time, it will also study how the hardware characteristics of LiDAR are related to multi-sensor fusion denoising and explore the advantages of vehicle-road cooperative perception. This paper will introduce the current situation of research at home and abroad on technical progress and provide theoretical support for the engineering optimisation and application of automotive LiDAR perception technology.