  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. Computational resources
and power budgets are limited on these robots, thereby motivating the
development of resource-efficient registration method on low-cost FPGAs. In
this paper, we propose a novel approach for FPGA-based 3D point cloud
registration built upon a recent deep learning-based method, PointNetLK. A
highly-efficient FPGA accelerator for PointNet-based feature extraction is
designed and implemented on both low-cost and mid-range FPGAs (Avnet Ultra96v2
and Xilinx ZCU104). Our accelerator design is evaluated in terms of
registration speed, accuracy, resource usage, and power consumption.
Experimental results show that PointNetLK with our accelerator achieves up to
21.34x and 69.60x faster registration speed than the CPU counterpart and ICP,
respectively, while only consuming 722mW and maintaining the same level of
accuracy.
