Stream-Based LiDAR Point Cloud Ground Segmentation for Autonomous Vehicle

This paper introduces a hardware-friendly refined depth ground segmentation method and its FPGA deployment, designed for real-time applications with stringent power and energy constraints. Our approach outperforms traditional CPU solutions, achieving over 10x lower power consumption and orders of magnitude reduction in energy usage per frame. Leveraging a one-pass stream-based architecture, the method supports various LiDAR configurations with low latency and consistent performance. Experimental results demonstrate superior segmentation accuracy in both point-wise and area-wise evaluations, establishing our approach as a practical and efficient solution for autonomous vehicle applications.