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Constrained submap algorithm for simultaneous localization and mapping

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Abstract

When solving the problem of simultaneous localization and mapping (SLAM), a standard extended Kalman filter (EKF) is subject to linearization errors and causes optimistic estimation. This paper proposes a submap algorithm, which builds a weighted least squares (WLS) constraint between two adjacent submaps according to the different estimations of the common features and the relationship between the vehicle poses in the corresponding submaps. By establishing the constraint equation after loop closing, re-linearization is implemented and each submap’s reference frame tends to its equilibrium position quickly. Experimental results demonstrate that the algorithm could get a globally consistent map and linearization errors are limited in local regions.

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Authors and Affiliations

  1. Research Institute of Robotics, Shanghai Jiaotong University, Shanghai, 200240, China

    Jun Qian  (钱 钧), Chen Wang  (王 晨), Ru-qing Yang  (杨汝清) & Chun-xiang Wang  (王春香)

  2. Department of Automation, Shanghai Jiaotong University, Shanghai, 200240, China

    Ming Yang  (杨 明)

Authors
  1. Jun Qian  (钱 钧)
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  2. Chen Wang  (王 晨)
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  3. Ming Yang  (杨 明)
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  4. Ru-qing Yang  (杨汝清)
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  5. Chun-xiang Wang  (王春香)
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Corresponding author

Correspondence to Ming Yang  (杨 明).

Additional information

Foundation item: the Knowledge Innovation Program of Shanghai Science and Technology Committee (No. 08510708300), and the Ph.D. Programs Foundation of Ministry of Education of China (No. 20070248097)

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Qian, J., Wang, C., Yang, M. et al. Constrained submap algorithm for simultaneous localization and mapping. J. Shanghai Jiaotong Univ. (Sci.) 14, 600–605 (2009). https://doi.org/10.1007/s12204-009-0600-7

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  • DOI: https://doi.org/10.1007/s12204-009-0600-7

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