Localization on a-priori information of plane extraction

Junjie Ji; Jing-Shan Zhao; Sergey Yurievich Misyurin; Daniel Martins

doi:10.1371/journal.pone.0285509

Localization on a-priori information of plane extraction

PLoS One. 2023 May 8;18(5):e0285509. doi: 10.1371/journal.pone.0285509. eCollection 2023.

Authors

Junjie Ji¹, Jing-Shan Zhao¹, Sergey Yurievich Misyurin^{2

3}, Daniel Martins⁴

Affiliations

¹ Department of Mechanical Engineering, Tsinghua University, Beijing, China.
² Moscow Engineering Physics Institute, National Research Nuclear University MEPhI, Moscow, Russia.
³ Blagonravov Mechanical Engineering Research Institute RAS, Moscow, Russia.
⁴ Department of Mechanical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil.

Abstract

Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall's surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.

Copyright: © 2023 Ji et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Grants and funding

This research was funded by 2020GQI1003, Guoqiang Research Institute of Tsinghua University. This is a special project the Grant number of which is '2020GQI1003'. The research of this paper is fully support by this Funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.