FPPS: An FPGA-Based Point Cloud Processing System

TL;DR

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, is presented, offering a compelling balance between performance and energy consumption.

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.

Figures (4)

• Figure 1: An overview of point cloud registration.
• Figure 2: The system diagram of FPPS.
• Figure 3: The task-level pipelined design of NN searcher.
• Figure 4: Post-routing device view of the accelerator on Alveo U50 (SLR0).