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Open AccessArticle

A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping

Department of Mechatronics Engineering, Kangwon National University, Chuncheon KR24341, Korea
Sensors 2019, 19(22), 4910; https://doi.org/10.3390/s19224910
Received: 15 October 2019 / Revised: 2 November 2019 / Accepted: 7 November 2019 / Published: 10 November 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
This paper presents an efficient revisiting algorithm for landmark-based simultaneous localization and mapping (SLAM). To reduce SLAM uncertainty in terms of a robot’s pose and landmark positions, the method autonomously evaluates valuable landmarks for the data associations in the SLAM algorithm and selects positions to revisit by considering both landmark visibility and sensor measurement uncertainty. The optimal path among the selected positions is obtained by applying the traveling salesman problem (TSP) algorithm. To plan a path that reduces overall uncertainty, the cost matrix associated with the change in covariance between all selected positions of all pairs is applied for the TSP algorithm. From simulations, it is verified that the proposed method efficiently reduces and maintains SLAM uncertainty at the low level compared to the backtracking method. View Full-Text
Keywords: mobile robot; simultaneous localization and mapping; traveling salesman problem; path planning; loop-closing; exploration mobile robot; simultaneous localization and mapping; traveling salesman problem; path planning; loop-closing; exploration
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Ryu, H. A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping. Sensors 2019, 19, 4910.

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