Employing FPGA to Implement NN Search in 3D-LiDAR: A Focus on Cache Architecture

3-D Light Detection and Ranging (3-D LiDAR) sensors are essential for autonomous vehicle functions such as localization, sensing, and mapping. However, their significant computational processing requirement remains a compelling drawback. Although high-end GPUs and CPUs could resolve the abovementioned issues, their prohibitive costs and substantial power requirements hinder the commercial adoption of 3-D LiDAR in vehicles. This paper presents a novel Nearest Neighbour (NN) searching method based on Field-Programmable Gate Arrays (FPGA) for 3-D LiDAR, designed for exceptional efficiency and accuracy. The method aims to offer a real-time LiDAR data processing solution, which delivers superior results on both GPU and CPU platforms. The proposed method consists of three parts: LiDAR data pre-processing, hardware-accelerate NN search, and efficient caching architecture for point cloud Data. The LiDAR processing methods successfully were implemented on the proposed FPGA platform. Experiment results show that our custom test board can accelerate NN searching beyond the capabilities of CPUs. Finally, the system we're proposing offers real-time functionality with a power consumption of just 2.8W. It upholds precision levels that can be compared favorably to software equivalents and even the latest LiDAR data processing techniques.