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Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm.

Tingchen Ma1,2, Lingxin Kong1,2,3, Yongsheng Ou4

  • 1Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

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|June 27, 2024
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Summary
This summary is machine-generated.

This study introduces a novel hash multi-scale (H-MS) map and a bidirectional matching algorithm (MSBM) to improve Simultaneous Localization and Mapping (SLAM) systems. The HMS-SLAM system demonstrates enhanced efficiency and accuracy in complex environments.

Keywords:
LiDAR SLAMbidirectional matchinghash multi-scale maprobot navigation

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision and Mapping

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for robotics, requiring efficient map models and accurate data association.
  • Existing SLAM methods face challenges in handling complex environments due to limitations in mapping efficiency and data association accuracy.
  • The need for robust SLAM solutions is driven by applications requiring precise environmental representation and real-time localization.

Purpose of the Study:

  • To propose a novel hash multi-scale (H-MS) map for efficient querying and accurate environmental modeling in SLAM.
  • To develop a bidirectional matching algorithm (MSBM) addressing the computational challenges of data association in multi-scale maps.
  • To integrate the H-MS map and MSBM algorithm into a LiDAR SLAM (HMS-SLAM) system and evaluate its performance.

Main Methods:

  • Developed a hash multi-scale (H-MS) map where map points model voxels at different scales, stored in a hash table for efficient access.
  • Proposed a bidirectional matching algorithm (MSBM) using forward-reverse-forward projection to balance data association efficiency and accuracy.
  • Integrated the H-MS map and MSBM into the HMS-SLAM system and validated it using the public KITTI dataset.

Main Results:

  • The H-MS map demonstrates superior insertion and deletion efficiency (O(1) time complexity) compared to the ikd-tree map.
  • The MSBM algorithm shows improved computational efficiency and accuracy over small-scale priority matching, achieving 49 ms/time on a single CPU thread.
  • The HMS-SLAM system achieved excellent performance in mapping accuracy and memory usage, outperforming existing methods.

Conclusions:

  • The proposed H-MS map and MSBM algorithm effectively address the limitations of previous SLAM approaches in complex scenarios.
  • The HMS-SLAM system offers a robust and efficient solution for real-time localization and mapping applications.
  • This research contributes to advancing the stability and performance of SLAM systems in diverse robotic applications.