A Self-localization Method for mobile robot Using VAE with 2D LiDAR Data Completion

2021 ◽  
Vol 2021 (0) ◽  
pp. 1P1-L08
Author(s):  
Kentaro FUKUDA ◽  
Takayuki NAKAMURA
Keyword(s):  
Mobile Robot ◽  
Lidar Data ◽  
Localization Method ◽  
Data Completion

Development of Autonomous Mobile Robot “MML-05” Based on i-Cart Mini for Tsukuba Challenge 2015

2016 ◽  
Vol 28 (4) ◽  
pp. 461-469 ◽  
Author(s):  
Tomoyoshi Eda ◽  
◽  
Tadahiro Hasegawa ◽  
Shingo Nakamura ◽  
Shin’ichi Yuta
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
The Self ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Localization Method ◽  
Occupancy Grid ◽  
Grid Map ◽  
Local Grid ◽  
Occupancy Grid Map

[abstFig src='/00280004/04.jpg' width='300' text='Autonomous mobile robots entered in the Tsukuba Challenge 2015' ] 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 self-localization. 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.

Enhancement of Scan Matching Using an Environmental Magnetic Field

2018 ◽  
Vol 30 (4) ◽  
pp. 532-539 ◽  
Author(s):  
Sam Ann Rahok ◽  
Hirohisa Oneda ◽  
Taichi Nakayama ◽  
Kazumichi Inoue ◽  
Shigeji Osawa ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Geomagnetic Field ◽  
Unique Property ◽  
Scan Matching ◽  
Localization Method ◽  
Robust Localization

Scan matching is one of the most reliable localization methods for mobile robots in known environments. However, an unexpected shift in posture remains its major issue. A method that uses an environmental magnetic field, a magnetic field that occurs in the environment, is presented to address this issue. The environmental magnetic field, which mostly refers to the geomagnetic field, is rarely changed by time. This unique property provides a means to enhance scan matching to provide a more robust localization method by using it to compensate the mobile robot’s pose. In this study, we describe how to compensate the mobile robot’s pose with the environmental magnetic field. Through experiments, we show that a mobile robot with the proposed method can recover, even if irregular changes in posture occur during the navigation.

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