J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiao Tong University(Science) ›› 2020, Vol. 25 ›› Issue (5): 650-658.doi: 10.1007/s12204-020-2201-4

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Low Data Overlap Rate Graph-Based SLAM with Distributed Submap Strategy

Low Data Overlap Rate Graph-Based SLAM with Distributed Submap Strategy

XIANG Jiawei, ZHANG Jinyi, WANG Bin, MA Yongbin    

  1. (1. Key laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;
    2. Institute of System Integration, Shanghai Sansi Electronic Engineering Co., Ltd., Shanghai 201100, China)
  2. (1. Key laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;
    2. Institute of System Integration, Shanghai Sansi Electronic Engineering Co., Ltd., Shanghai 201100, China)
  • Online:2020-10-28 Published:2020-09-11
  • Contact: XIANG Jiawei (相家炜) E-mail:xiang_jiawei@163.com

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Abstract:  Simultaneous localization and mapping (SLAM) is widely used in many robot applications to acquire
the unknown environment’s map and the robot’s location. Graph-based SLAM is demonstrated to be effective in
large-scale scenarios, and it intuitively performs the SLAM as a pose graph. But because of the high data overlap
rate, traditional graph-based SLAM is not efficient in some respects, such as real time performance and memory
usage. To reduce data overlap rate, a graph-based SLAM with distributed submap strategy (DSS) is presented.
In its front-end, submap based scan matching is processed and loop closing detection is conducted. Moreover in
its back-end, pose graph is updated for global optimization and submap merging. From a series of experiments, it
is demonstrated that graph-based SLAM with DSS reduces 51.79% data overlap rate, decreases 39.70% runtime
and 24.60% memory usage. The advantages over other low overlap rate method is also proved in runtime, memory
usage, accuracy and robustness performance.

Key words: graph-based SLAM| distributed submap strategy| data overlap rate

摘要：  Simultaneous localization and mapping (SLAM) is widely used in many robot applications to acquire
the unknown environment’s map and the robot’s location. Graph-based SLAM is demonstrated to be effective in
large-scale scenarios, and it intuitively performs the SLAM as a pose graph. But because of the high data overlap
rate, traditional graph-based SLAM is not efficient in some respects, such as real time performance and memory
usage. To reduce data overlap rate, a graph-based SLAM with distributed submap strategy (DSS) is presented.
In its front-end, submap based scan matching is processed and loop closing detection is conducted. Moreover in
its back-end, pose graph is updated for global optimization and submap merging. From a series of experiments, it
is demonstrated that graph-based SLAM with DSS reduces 51.79% data overlap rate, decreases 39.70% runtime
and 24.60% memory usage. The advantages over other low overlap rate method is also proved in runtime, memory
usage, accuracy and robustness performance.

关键词: graph-based SLAM| distributed submap strategy| data overlap rate

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