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An Odometer-Free Approach for Unmanned Ground-Based Vehicle Simultaneous Localization and Mapping

Gu, Xiaowei, Angelov, Muhammad Aurangzeb (2019) An Odometer-Free Approach for Unmanned Ground-Based Vehicle Simultaneous Localization and Mapping. In: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference. . (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90193)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
http://hdl.handle.net/2160/de2b574c-9a4f-4d73-9b09...

Abstract

Simultaneous localization and mapping (SLAM) in unknown GPS-denied environments is a very challenging problem due to the complex environment factors and the lack of prior knowledge of such environments. The performance of standard SLAM methods is dependent on odometer measurements, which, however, are unlikely to be reliable in a challenging operating environment. In this paper, a novel odometer-free approach is introduced for unmanned ground-based vehicle (UGV) to perform SLAM using only LIDAR/SONAR scans in the form of discrete point clouds. The proposed odometer-free SLAM (OF-SLAM) approach can precisely align successive sensor scans without involving other auxiliary information, e.g., odometer readings. By converting the accurately aligned point clouds into continuous local grid maps using kernel tricks, OF-SLAM creates a dynamically updating global map of the surrounding environment and further accurately localizes the UGV on the map. Simulation experiments verify the validity and effectiveness of OF-SLAM and demonstrate the proposed approach as an attractive alternative for UGV to perform SLAM and explore complex unknown environments.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 08:23 UTC
Last Modified: 15 Sep 2021 16:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90193 (The current URI for this page, for reference purposes)

University of Kent Author Information

Gu, Xiaowei.

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