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JRM Vol.29 No.5 pp. 928-934
doi: 10.20965/jrm.2017.p0928
(2017)

Development Report:

Performance Evaluation of Robot Localization Using 2D and 3D Point Clouds

Kiyoaki Takahashi, Takafumi Ono, Tomokazu Takahashi, Masato Suzuki, Yasuhiko Arai, and Seiji Aoyagi

Kansai University
3-3-35 Yamate-cho, Suita, Osaka 564-8680, Japan

Received:
March 6, 2017
Accepted:
August 22, 2017
Published:
October 20, 2017
Keywords:
point cloud, self-localization, autonomous mobile robot
Abstract
Performance Evaluation of Robot Localization Using 2D and 3D Point Clouds

Evaluation of robot localization using point clouds

Autonomous mobile robots need to acquire surrounding environmental information based on which they perform their self-localizations. Current autonomous mobile robots often use point cloud data acquired by laser range finders (LRFs) instead of image data. In the virtual robot autonomous traveling tests we have conducted in this study, we have evaluated the robot’s self-localization performance on Normal Distributions Transform (NDT) scan matching. This was achieved using 2D and 3D point cloud data to assess whether they perform better self-localizations in case of using 3D or 2D point cloud data.

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Last updated on Dec. 12, 2017