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

Kiyoaki Takahashi | Zendy; Takafumi Ono | Zendy; Tomokazu Takahashi | Zendy; Masato Suzuki | Zendy; Yasuhiko Arai | Zendy; Seiji Aoyagi | Zendy

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

Author(s) -

Kiyoaki Takahashi,

Takafumi Ono,

Tomokazu Takahashi,

Masato Suzuki,

Yasuhiko Arai,

Seiji Aoyagi

Publication year - 2017

Publication title -

journal of robotics and mechatronics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.257

H-Index - 19

eISSN - 1883-8049

pISSN - 0915-3942

DOI - 10.20965/jrm.2017.p0928

Subject(s) - point cloud , computer vision , mobile robot , robot , artificial intelligence , computer science , point (geometry) , matching (statistics) , range (aeronautics) , engineering , mathematics , geometry , statistics , aerospace engineering

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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