NDLI: Long range traversable region detection based on superpixels clustering for mobile robots

Content Provider	IEEE Xplore Digital Library
Author	Huimin Lu Lixing Jiang Zell, A.
Copyright Year	2015
Description	Author affiliation: Coll. of Mechatron. & Autom., Nat. Univ. of Defense Technol., Changsha, China (Huimin Lu) \|\| Dept. of Cognitive Syst., Univ. of Tubingen, Tubingen, Germany (Lixing Jiang; Zell, A.)
Abstract	Traversable region detection is important for autonomous visual navigation of mobile robots. Only short range traversable regions can be detected using traditional methods based on stereo vision because of the limited image resolution and baseline of stereo vision. In this paper, we propose a novel method to detect long range traversable regions without using any supervised or self-supervised learning process. Superpixels are clustered using an improved spectral clustering algorithm to segment the image effectively, and after integrating short range traversable region detection based on u-v-disparity, the traversable region can be extended to long range naturally. The experimental results show that the proposed method works well in different outdoor/field environments, and the detecting range can be improved greatly in comparison with traditional methods. Furthermore, the proposed superpixels clustering algorithm can also be applied in other robot vision tasks like road detection and object recognition.
Starting Page	546
Ending Page	552
File Size	1326272
Page Count	7
File Format	PDF
e-ISBN	9781479999941
DOI	10.1109/IROS.2015.7353425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-28
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Image color analysis Stereo vision Image segmentation Correlation Feature extraction Histograms
Content Type	Text
Resource Type	Article

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