Parallel Lidar Ground Segmentation: From Mechanical to Solid-State Sensors

Real-time and efficient LiDAR ground segmentation is crucial for autonomous perception and edge computing applications. This paper introduces an FPGA-based parallel segmentation framework that significantly reduces computational latency while maintaining high accuracy. Through a stream-pipelined design, the proposed method achieves significant speedup over CPU-based implementations while benefiting from the denser and more evenly distributed point clouds of Solid-State LiDAR (SSL). Extensive evaluations on the SemanticKITTI dataset and a self-collected SSL dataset demonstrate the effectiveness of our approach across both mechanical and SSL systems, highlighting its potential for real-world deployment in autonomous vehicles and edge-based perception platforms.