An FPGA-based kNN Seach Accelerator for point cloud registration

Point cloud registration assumes a crucial role in several fields, including 3D reconstruction and pose estimation. The prevalent technique for point cloud registration is Iterative Closest Point (ICP). Nonetheless, the k-nearest neighbor (kNN) search, an essential component of the ICP process, often falls short in meeting real-time requirements due to its substantial time consumption. Consequently, extensive efforts have been dedicated to accelerating the kNN search within the ICP framework. This study proposes an FPGA-based kNN Search Accelerator, leveraging an improved LSH approach to expedite point cloud access and search. Experimental results underscore its superiority with a 120x and 15x speed-up compared to CPU and GPU implementations of the kNN process, respectively. Remarkably, the kNN search completes in a mere 0.64 ms, surpassing the performance of prior works.