An Energy-Efficient and Real-Time FPGA-Based Point Cloud Registration Framework with Ultra-Fast and Configurable Multi-Mode Correspondence Search

Point cloud registration is a fundamental task in LiDAR-based localization and mapping, widely employed in robotics and autonomous vehicles. However, existing registration solutions lose geometric topology continuity and lack scanline-aware, configurable correspondence search, restricting their real-time applicability. To solve these issues, we propose a fundamentally re-architected, energy-efficient FPGA framework for real-time point cloud registration, featuring a configurable, multi-mode correspondence search engine. First, we introduce a scanline-aided range-projection structure (SA-RPS) that reorganizes LiDAR points within configurable segmentation domains into contiguous memory while preserving scanline topology, enabling efficient and flexible multi-mode correspondence search. Second, we develop a deeply pipelined, ultra-fast SA-RPS-based correspondence search (SA-RPS-CS) accelerator that supports dynamic configuration of search mode and parallelism and incorporates a sliding-window cache and scanline-aware K-selection module for high-throughput, multi-mode correspondence extraction. Third, we present a co-designed registration framework that integrates the accelerator with dynamic parameter configuration, enabling adaptive, real-time processing across diverse SLAM scenarios. Experimental results demonstrate that the proposed SA-RPS-CS accelerator delivers \(2.3\times\) – \(32.4\times\) faster search and \(1.8\times\) – \(26.2\times\) higher energy efficiency than previous state-of-the-art FPGA designs, achieving real-time registration for 64-channel LiDAR at 21.5 FPS with negligible loss in accuracy.