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Autonomous Robots
Erschienen in: Autonomous Robots 6/2020

13.02.2020

GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction

verfasst von: Bo Li, Yingqiang Wang, Yu Zhang, Wenjie Zhao, Jianyuan Ruan, Ping Li

Erschienen in: Autonomous Robots | Ausgabe 6/2020

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Abstract

Existing laser-based 2D simultaneous localization and mapping (SLAM) methods exhibit limitations with regard to either efficiency or map representation. An ideal method should estimate the map of the environment and the state of the robot quickly and accurately while providing a compact and dense map representation. In this study, we develop a new laser-based SLAM algorithm by redesigning the two core elements common to all SLAM systems, namely the state estimation and map construction. Utilizing Gaussian process (GP) regression, we propose a new type of map representation based on the regionalized GP map reconstruction algorithm. With this new map representation, both the state estimation method and the map update method can be completed with the use of concise mathematics. For small- or medium-scale scenarios, our method, consisting of only state estimation and map construction, demonstrates outstanding performance relative to traditional occupancy-grid-map-based approaches in both accuracy and especially efficiency. For large-scale scenarios, we extend our approach to a graph-based version.
Vorheriger Artikel Improved and scalable online learning of spatial concepts and language models with mapping
Nächster Artikel Correction to: GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction

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Metadaten
Titel
GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction
verfasst von
Bo Li
Yingqiang Wang
Yu Zhang
Wenjie Zhao
Jianyuan Ruan
Ping Li
Publikationsdatum
13.02.2020
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 6/2020
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-020-09906-z

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