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Multifeature Image Indexing for Robot Localization in Textureless Environments

1
Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajinocho, Koganei, Tokyo 184-8584, Japan
2
Fairy Devices Inc, Tokyo 113-0034, Japan
3
Department of Electrical and Control Systems Engineering, National Institute of Technology, Toyama College, Toyama 939-8046, Japan
*
Author to whom correspondence should be addressed.
Robotics 2019, 8(2), 37; https://doi.org/10.3390/robotics8020037
Received: 29 March 2019 / Revised: 25 April 2019 / Accepted: 30 April 2019 / Published: 3 May 2019
Robot localization is an important task for mobile robot navigation. There are many methods focused on this issue. Some methods are implemented in indoor and outdoor environments. However, robot localization in textureless environments is still a challenging task. This is because in these environments, the scene appears the same in almost every position. In this work, we propose a method that can localize robots in textureless environments. We use Histogram of Oriented Gradients (HOG) and Speeded Up Robust Feature (SURF) descriptors together with Depth information to form a Depth-HOG-SURF multifeature descriptor, which is later used for image matching. K-means clustering is applied to partition the whole feature into groups that are collectively called visual vocabulary. All the images in the database are encoded using the vocabulary. The experimental results show a good performance of the proposed method. View Full-Text
Keywords: localization; depth camera; image indexing; HOG feature; SURF feature localization; depth camera; image indexing; HOG feature; SURF feature
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Dung, T.D.; Hossain, D.; Kaneko, S.-I.; Capi, G. Multifeature Image Indexing for Robot Localization in Textureless Environments. Robotics 2019, 8, 37.

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