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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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| 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)
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Submitter: Abhijeet Ravankar
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