HeLPS: Heterogeneous LiDAR-based Positioning System for Autonomous Vehicle

LiDAR-based positioning systems are widely used in unmanned systems. However, affected by the high computational complexity of high-precision positioning algorithms, the current positioning system is supported by hardware with low power efficiency and thus hard to integrate into many platforms. In this paper, we analyze features of the positioning system in autonomous driving application, design, and apply Heterogeneous LiDAR-based Positioning System, HeLPS, with software-hardware co-design methodology to achieve better efficiency. Our contributions can be concluded in three aspects. Firstly, we design the CPU-FPGA heterogeneous positioning system accelerating Iterative Closest Point (ICP) algorithm and achieves improvements on both speed and power-efficiency. Secondly, we exploit the spatial locality in the point cloud and design a new compressed data structure for fast neighbor accessing. The experiment reports a significant speedup comparing with other data structures. Lastly, we explore the data access pattern in positioning application and develop a specific cache system and out-of-order execution system reducing memory burden. Our system is deployed on a small and cheap Xilinx Zynq7000 ARM+FPGA platform, which achieves 983.3x speedup compared with Cortex-A9 CPU, and 31.8x speedup compared with i7-7820 CPU, with only 2.37W power consumption.