论文标题
2D激光雷达算法的FPGA加速度和优化技术
An FPGA Acceleration and Optimization Techniques for 2D LiDAR SLAM Algorithm
论文作者
论文摘要
对于具有有限的计算资源的移动自主机器人来说，有效的同时本地化和映射（SLAM）方法的高效硬件实现是必要的。在本文中，我们提出了一种资源有效的FPGA实现，用于加速扫描匹配计算，该计算通常会在2D LIDAR SLAM方法中引起主要的瓶颈。扫描匹配是通过将最新的LiDAR测量值与占用网格图对齐来纠正机器人姿势的过程，该测量值编码有关周围环境的信息。我们利用基于Rao-Blackwellized粒子滤波器（RBPF）算法的固有的并行性来对并联多个粒子进行扫描匹配计算。在拟议的设计中，采用了几种技术来减少资源利用并实现最大吞吐量。使用基准数据集的实验结果表明，扫描匹配被5.31-8.75x加速，整体吞吐量提高了3.72-5.10x，而不会严重降低最终输出的质量。此外，我们提出的IP核心仅需要TUL PYNQ-Z2 FPGA董事会中可用的总资源的44％，从而促进了在室内移动机器人上实现SLAM应用程序的实现。
An efficient hardware implementation for Simultaneous Localization and Mapping (SLAM) methods is of necessity for mobile autonomous robots with limited computational resources. In this paper, we propose a resource-efficient FPGA implementation for accelerating scan matching computations, which typically cause a major bottleneck in 2D LiDAR SLAM methods. Scan matching is a process of correcting a robot pose by aligning the latest LiDAR measurements with an occupancy grid map, which encodes the information about the surrounding environment. We exploit an inherent parallelism in the Rao-Blackwellized Particle Filter (RBPF) based algorithms to perform scan matching computations for multiple particles in parallel. In the proposed design, several techniques are employed to reduce the resource utilization and to achieve the maximum throughput. Experimental results using the benchmark datasets show that the scan matching is accelerated by 5.31-8.75x and the overall throughput is improved by 3.72-5.10x without seriously degrading the quality of the final outputs. Furthermore, our proposed IP core requires only 44% of the total resources available in the TUL Pynq-Z2 FPGA board, thus facilitating the realization of SLAM applications on indoor mobile robots.
