An FPGA Acceleration and Optimization Techniques for 2D LiDAR SLAM Algorithm

Keisuke Sugiura, Hiroki Matsutani

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 algorithm 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 5.31-8.75× and the overall throughput is improved by 3.72-5.10× 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.

Original languageEnglish
Pages (from-to)789-800
Number of pages12
JournalIEICE Transactions on Information and Systems
Issue number6
Publication statusPublished - 2021


  • FPGA
  • GMapping
  • SLAM
  • SoC

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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