Premium
3D Scan matching for mobile robot localization over rough terrain
Author(s) -
Nakagomi Hiroyuki,
Fuse Yoshihiro,
Hosaka Hidehiko,
Miyamoto Hironaga,
Nakamura Takashi,
Yoneyama Akira,
Yokotsuka Masashi,
Kamimura Akiya,
Watanabe Hiromi,
Tanzawa Tsutomu,
Kotani Shinji
Publication year - 2019
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.23250
Subject(s) - odometry , computer vision , terrain , artificial intelligence , mobile robot , computer science , robot , iterative closest point , position (finance) , matching (statistics) , feature (linguistics) , point (geometry) , tracking (education) , point cloud , mathematics , geography , psychology , pedagogy , linguistics , statistics , philosophy , geometry , cartography , finance , economics
In order to enable an autonomous mobile robot to travel over rough terrain, it necessitates the capability to detect self‐position accurately even when the odometry errors are increased in traveling. The conventional method can keep high speed and precise localization using iterative closest point algorithms or feature matching techniques. However, effects of steep changes of a mobile robot position are not considered when it travels over rough terrain. In this article, we propose the method for efficient real‐time 6D pose tracking using a rotating 2D laser scanner in traveling over rough terrain. For adaptation to steep changes of the position, weighted point clouds are generated based on the angular and the linear velocity measured by sensors mounted on the robot. And the position and posture of the robot are sequentially optimized by the scan matching in increments of 10 scans. In indoor experiments, we evaluated accuracy of our method when the robot passes on rugged floor. As a result, our method was performed with less than 0.078 m RMS positional error in real time.