Stream-Based Ground Segmentation for Real-Time LiDAR Point Cloud Processing on FPGA

TL;DR

This paper introduces a fast, FPGA-optimized ground segmentation method for LiDAR point clouds that achieves real-time processing with high accuracy and significantly outperforms CPU and GPU solutions in speed and power efficiency.

Contribution

The paper presents a novel FPGA-based stream processing approach for LiDAR ground segmentation, combining innovative algorithms and hardware architecture for real-time performance.

Findings

Achieved 12-25x faster processing than CPU-based solutions.

Demonstrated high accuracy in ground segmentation on SemanticKITTI dataset.

Implemented a flexible FPGA architecture compatible with various LiDAR channel densities.

Abstract

This paper presents a novel and fast approach for ground plane segmentation in a LiDAR point cloud, specifically optimized for processing speed and hardware efficiency on FPGA hardware platforms. Our approach leverages a channel-based segmentation method with an advanced angular data repair technique and a cross-eight-way flood-fill algorithm. This innovative approach significantly reduces the number of iterations while ensuring the high accuracy of the segmented ground plane, which makes the stream-based hardware implementation possible. To validate the proposed approach, we conducted extensive experiments on the SemanticKITTI dataset. We introduced a bird's-eye view (BEV) evaluation metric tailored for the area representation of LiDAR segmentation tasks. Our method demonstrated superior performance in terms of BEV areas when compared to the existing approaches. Moreover, we presented an optimized hardware architecture targeted on a Zynq-7000 FPGA, compatible with LiDARs of various channel densities, i.e., 32, 64, and 128 channels. Our FPGA implementation operating at 160 MHz significantly outperforms the traditional computing platforms, which is 12 to 25 times faster than the CPU-based solutions and up to 6 times faster than the GPU-based solution, in addition to the benefit of low power consumption.