Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping release_r5b2n5sl5zfajmswlgvcaay7bu

by Dongxiao Han, Yuwen Li, Tao Song, Zhenyang Liu

Published in Sensors by MDPI AG.

2020   Volume 20, Issue 7, p1906

Abstract

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.
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Type  article-journal
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Date   2020-03-30
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DOI  10.3390/s20071906
PubMed  32235456
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