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.

翻译：点云处理是自动驾驶系统中的计算瓶颈，尤其在实时应用中更为突出，而能效仍是关键的系统约束。本文提出FPPS，一种基于FPGA加速的点云处理系统，旨在优化迭代最近点（ICP）算法——该算法是三维定位与感知流程中的经典基石。在广泛使用的KITTI基准数据集上进行评估，所提出的系统相比先进的CPU基线实现了高达35倍（以及运行时加权平均15.95倍）的加速，同时保持相当的配准精度。值得注意的是，该设计将平均能效提升了8.58倍，在性能与能耗之间提供了引人注目的平衡。这些成果使FPPS成为资源受限的嵌入式自动驾驶平台的一种可行解决方案，其中延迟与功耗均为关键的设计优先考量。