Article
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Preserved in Portico This version is not peer-reviewed
Generative Cloud of Points SLAM (G-SLAM)
Version 1
: Received: 30 January 2019 / Approved: 1 February 2019 / Online: 1 February 2019 (04:04:07 CET)
How to cite: Zikos, N.; Petridis, V. Generative Cloud of Points SLAM (G-SLAM). Preprints 2019, 2019020003. https://doi.org/10.20944/preprints201902.0003.v1 Zikos, N.; Petridis, V. Generative Cloud of Points SLAM (G-SLAM). Preprints 2019, 2019020003. https://doi.org/10.20944/preprints201902.0003.v1
Abstract
Mobile robots need an environmental perception ability in order to interact with the surrounding environment. In this paper we present the G-SLAM method, where the map is consisted of a cloud of scattered points in the continuous space and each point is accompanied by an obstacle existence probability. On the other hand the robot's pose (and trajectory) is estimated by an particle filters while the cloud of points is estimated by an adaptive recursive algorithm which is presented. Main feature of the presented method is the adaptive repositioning of the scattered points and their convergence around obstacles. In addition to our previous publications, In this paper a mathematical derivation of the method is presented and real case scenarios are discussed, presented and compared with other methods.
Keywords
SLAM; G-SLAM; particle filters; monte carlo
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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