FPGA-based Acceleration Applied to Spherical Projection and Distance Image Feature Extraction Algorithms in 3D LiDAR Odometry for Autonomous Driving

With the rapid development of autonomous driving technology, LiDAR (Light Detection and Ranging) has gradually become a mainstream tool for vehicle positioning and navigation. LiDAR odometry relies on the processing and feature extraction of 3D point cloud data to achieve high-precision environmental perception and path planning. Traditional computational methods face significant bottlenecks when handling these data, particularly in terms of real-time processing and hardware acceleration. To address this, this paper proposes a hardware implementation scheme for 3D LiDAR odometry accelerated by FPGA, with a focus on spherical projection and distance image-based feature extraction algorithms.First, we designed and implemented a multi-core parallel FPGA hardware architecture, which includes two main modules: the multi-core parallel spherical projection hardware module and the normal vector feature extraction module. By optimizing the hardware architecture and leveraging FPGA’s parallel processing capabilities, we accelerated the computation process, significantly improving the data processing speed.Experimental results show that, compared to traditional CPU processing methods, the FPGA-based acceleration scheme demonstrates significant acceleration in both spherical projection and feature extraction, with processing time greatly reduced. By comparing the processing times across different platforms, we validated the potential of FPGA in efficiently processing LiDAR data, providing an effective solution for hardware acceleration in 3D LiDAR data processing for autonomous driving systems.The findings of this paper offer a reference path for future FPGA-based hardware acceleration implementations in autonomous driving systems and open new directions for hardware optimization and applications in related fields.