Memory Efficient Point Cloud Registration Accelerator on FPGA

Abstract

Point cloud registration, which aligns multiple datasets into a unified coordinate system, is critical for mobile applications such as 3D SLAM and autonomous driving. Among existing methods, Iterative Closest Point (ICP) remains a widely used method for rigid registration due to its robustness and simplicity. However, its performance on mobile platforms is hindered by iterative computations and limited memory resources. This paper proposes a high-performance ICP registration framework implemented on FPGA. Building upon an efficient GPU-based method named VAN-ICP, our FPGA-based ICP accelerator achieves greater memory efficiency and faster processing speed, making it ideal for resource-constrained mobile platforms. Experimental results demonstrate a speedup of over 1.5× compared to mobile GPU-based implementations and a 99% reduction in memory usage, validating the effectiveness of the proposed approach for real-world point cloud registration on edge platforms. Beyond these improvements, the proposed framework also facilitates advancements in robotic vision technologies by enabling more accurate and efficient perception under stringent hardware constraints.