An Efficient Accelerator for Deep Learning-based Point Cloud Registration on FPGAs

Abstract

Point cloud registration is the basis for many robotic applications such as odometry and Simultaneous Localization And Mapping (SLAM), which are increasingly important for autonomous mobile robots. The limitation of computational resources and power budgets on such robots motivates us to study the resource-efficient registration method on low-cost edge devices. In this paper, we propose an FPGA-based novel pipeline for 3D point cloud registration built upon a recent deep learning-based method, PointNetLK. Based on the profiling results, we focus on the PointNet feature extraction as it becomes a major bottleneck; we improve its scalability and memory-efficiency by consuming each input point one-by-one in a pipelined manner instead of processing the whole point cloud at once. We then design a fully-parallelized and pipelined accelerator consisting of a custom PointNet IP core, which fits within both low-cost and mid-range FPGAs (e.g., Avnet Ultra96v2 and Xilinx ZCU104). Experimental results show that our proposed pipeline achieves up to 21.34x and 69.60x faster registration speed than the vanilla PointNetLK and ICP, respectively, while only consuming 722mW and maintaining the same level of accuracy.