FPPS: An FPGA-Based Point Cloud Processing System

TL;DR

FPPS is an FPGA-based system that accelerates point cloud processing, especially the ICP algorithm, achieving significant speed and energy efficiency improvements for real-time autonomous driving applications.

Contribution

The paper introduces FPPS, a novel FPGA-accelerated platform that significantly enhances point cloud processing speed and energy efficiency while maintaining accuracy.

Findings

Up to 35x speedup over CPU baseline

8.58x improvement in power efficiency

Maintains registration accuracy

Abstract

Point cloud processing is a computational bottleneck in autonomous driving systems, especially for real-time applications, while energy efficiency remains a critical system constraint. This work presents FPPS, an FPGA-accelerated point cloud processing system designed to optimize the iterative closest point (ICP) algorithm, a classic cornerstone of 3D localization and perception pipelines. Evaluated on the widely used KITTI benchmark dataset, the proposed system achieves up to 35$\times$ (and a runtime-weighted average of 15.95x) speedup over a state-of-the-art CPU baseline while maintaining equivalent registration accuracy. Notably, the design improves average power efficiency by 8.58x, offering a compelling balance between performance and energy consumption. These results position FPPS as a viable solution for resource-constrained embedded autonomous platforms where both latency and power are key design priorities.