A range ambiguity classification algorithm for automotive LiDAR based on FPGA platform acceleration - Li, Haoyu; Xiang, Sihua; Zhang, Lu; Zhu, Jianzhong; Wang, Song; Wang, You (Frontiers in Physics, 2023)
The automotive LiDAR industry has been experiencing a rapid expansion stage since 2022, with many researchers actively involved in LiDAR research and its installation in vehicles as a means of enhancing autopilot capabilities. The basic principle of LiDAR is similar to that of a laser rangefinder, where the distance information can be calculated by locating the echo instant corresponding to the laser emission moment. However, if the interval between laser light emissions is extremely narrow, the regions of light emission and the echo may overlap, resulting in a range ambiguity and an abnormal distance information calculation process. Additionally, the high resolution of LiDAR is characterized by its extremely high emission frequency, which allows for more accurate retrieval of information about the surrounding environment. However, this also means that the possibility of range ambiguity is increased. To address the range ambiguity problem in automotive LiDAR, this paper proposes an algorithm based on classification that can be accelerated by FPGA, making it the first of its kind in the field of automotive LiDAR. The algorithm can be performed using a single-wavelength pulsed laser and has been optimized specifically for the demands of FPGA. While maintaining the high resolution of LiDAR, it can overcome the attenuation of measurement ability caused by range ambiguity and also meet the demand for processing large amounts of point cloud information data at high speed. Furthermore, the cost of the entire device can be controlled while significantly improving the performance of the LiDAR.