Abstract
This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating selflocalization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.
| Language | English |
|---|---|
| Pages | 461-469 |
| Number of pages | 9 |
| Journal | Journal of Robotics and Mechatronics |
| Volume | 28 |
| Issue number | 4 |
| State | Published - 2016 Aug 1 |
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Keywords
- Autonomous mobile robot
- Downhill simplex method
- Map matching
- Tsukuba challenge 2015
- Wheel odometry
ASJC Scopus subject areas
- Computer Science(all)
- Electrical and Electronic Engineering
Cite this
Development of autonomous mobile robot “MML-05” based on i-Cart mini for Tsukuba challenge 2015. / Eda, Tomoyoshi; Hasegawa, Tadahiro; Nakamura, Shingo; Yuta, Shin’Ichi.
In: Journal of Robotics and Mechatronics, Vol. 28, No. 4, 01.08.2016, p. 461-469.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Development of autonomous mobile robot “MML-05” based on i-Cart mini for Tsukuba challenge 2015
AU - Eda,Tomoyoshi
AU - Hasegawa,Tadahiro
AU - Nakamura,Shingo
AU - Yuta,Shin’Ichi
PY - 2016/8/1
Y1 - 2016/8/1
N2 - This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating selflocalization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.
AB - This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating selflocalization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.
KW - Autonomous mobile robot
KW - Downhill simplex method
KW - Map matching
KW - Tsukuba challenge 2015
KW - Wheel odometry
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M3 - Article
VL - 28
SP - 461
EP - 469
JO - Journal of Robotics and Mechatronics
T2 - Journal of Robotics and Mechatronics
JF - Journal of Robotics and Mechatronics
SN - 0915-3942
IS - 4
ER -