TY - GEN
T1 - DaHOG-based mobile robot indoor global localization
AU - Cheong, Howon
AU - Kim, Euntai
AU - Park, Sung Kee
N1 - Publisher Copyright:
© 2020 Institute of Control, Robotics, and Systems - ICROS.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10/13
Y1 - 2020/10/13
N2 - This paper suggests an indoor environment descriptor and global localization strategies for indoor robot navigation using a metric sensor and mono camera. Other researches use various feature descriptors (i.e. geometric features, visual local invariant features, and objects) for robot pose estimation. However, most of the real environments have repeated similar texture patterns or few objects although they need salient information for successful localization. To overcome this problem, we suggest a new environment descriptor, which is composed of the histogram of oriented gradient(HOG) and approximated 2D-polar coordinate distance of visual vertical edges. We call it Distance-assisted HOG (DaHOG). For the matching process, we use the omnidirectional datasets that have a circular pattern matching problem. Here, we solve the problem by proposing a new global localization method based on a spectral matching technique. We show that our method is effective with experiments in real environments where there is a lack of distinctive features and objects.
AB - This paper suggests an indoor environment descriptor and global localization strategies for indoor robot navigation using a metric sensor and mono camera. Other researches use various feature descriptors (i.e. geometric features, visual local invariant features, and objects) for robot pose estimation. However, most of the real environments have repeated similar texture patterns or few objects although they need salient information for successful localization. To overcome this problem, we suggest a new environment descriptor, which is composed of the histogram of oriented gradient(HOG) and approximated 2D-polar coordinate distance of visual vertical edges. We call it Distance-assisted HOG (DaHOG). For the matching process, we use the omnidirectional datasets that have a circular pattern matching problem. Here, we solve the problem by proposing a new global localization method based on a spectral matching technique. We show that our method is effective with experiments in real environments where there is a lack of distinctive features and objects.
UR - http://www.scopus.com/inward/record.url?scp=85098086559&partnerID=8YFLogxK
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U2 - 10.23919/ICCAS50221.2020.9268378
DO - 10.23919/ICCAS50221.2020.9268378
M3 - Conference contribution
AN - SCOPUS:85098086559
T3 - International Conference on Control, Automation and Systems
SP - 827
EP - 832
BT - 2020 20th International Conference on Control, Automation and Systems, ICCAS 2020
PB - IEEE Computer Society
T2 - 20th International Conference on Control, Automation and Systems, ICCAS 2020
Y2 - 13 October 2020 through 16 October 2020
ER -