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On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping


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タイトル: On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping
著者: Ravankar, Abhijeet 著作を一覧する
Ravankar, Ankit A. 著作を一覧する
Hoshino, Yohei 著作を一覧する
Emaru, Takanori 著作を一覧する
Kobayashi, Yukinori 著作を一覧する
キーワード: Line-segment Detection
Singular Value Decomposition
Hough Transform
Data Association
Simultaneous Localization and Mapping (SLAM)
発行日: 2016年 6月17日
出版者: SAGE Publications
誌名: International Journal of Advanced Robotic Systems
巻: 13
号: 3
開始ページ: 98
出版社 DOI: 10.5772/63540
抄録: Line detection is an important problem in computer vision, graphics and autonomous robot navigation. Lines detected using a laser range sensor (LRS) mounted on a robot can be used as features to build a map of the environment, and later to localize the robot in the map, in a process known as Simultaneous Localization and Mapping (SLAM). We propose an efficient algorithm for line detection from LRS data using a novel hopping-points Singular Value Decomposition (SVD) and Hough transform-based algorithm, in which SVD is applied to intermittent LRS points to accelerate the algorithm. A reverse-hop mechanism ensures that the end points of the line segments are accurately extracted. Line segments extracted from the proposed algorithm are used to form a map and, subsequently, LRS data points are matched with the line segments to localize the robot. The proposed algorithm eliminates the drawbacks of point-based matching algorithms like the Iterative Closest Points (ICP) algorithm, the performance of which degrades with an increasing number of points. We tested the proposed algorithm for mapping and localization in both simulated and real environments, and found it to detect lines accurately and build maps with good self-localization.
資料タイプ: article
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: Abhijeet Ravankar


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