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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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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65127

Title: On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping
Authors: Ravankar, Abhijeet Browse this author
Ravankar, Ankit A. Browse this author
Hoshino, Yohei Browse this author →KAKEN DB
Emaru, Takanori Browse this author →KAKEN DB
Kobayashi, Yukinori Browse this author →KAKEN DB
Keywords: Line-segment Detection
Singular Value Decomposition
Hough Transform
Data Association
Simultaneous Localization and Mapping (SLAM)
Issue Date: 17-Jun-2016
Publisher: SAGE Publications
Journal Title: International Journal of Advanced Robotic Systems
Volume: 13
Issue: 3
Start Page: 98
Publisher DOI: 10.5772/63540
Abstract: 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.
Rights: https://creativecommons.org/licenses/by/3.0/
Type: article
URI: http://hdl.handle.net/2115/65127
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: Abhijeet Ravankar

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