DocumentCode :
1783209
Title :
Traversable region detection based on near-to-far self-supervised incremental learning
Author :
Yunlei Chen ; Huimin Lu ; Junhao Xiao ; Hui Zhang
Author_Institution :
Coll. of Mechatron. & Autom., Nat. Univ. of Defense Technol., Changsha, China
fYear :
2014
fDate :
28-29 Sept. 2014
Firstpage :
1
Lastpage :
6
Abstract :
Effective far-range traversable region detection is a fundamental issue for mobile robots. However, the performance of traditional methods is limited as distance estimation of stereovision system is unreliable beyond 10-15m. In this paper, we proposed a far-range traversable region detection algorithm based on near-to-far self-supervised learning. In the algorithm, superpixel segmentation is employed as preprocessing to reduce the computational complexity. Then, near-range LBP features are extracted for each superpixel. Afterwards, the resulted LBP features are used to train an Incremental Supported Vector Machine (ISVM) for classification, which enhances the far-range region classification performance. Thorough experiments have been carried out utilizing our Nubot mobile robot in outdoor environments. Furthermore the proposed algorithm has also been evaluated using the KITTI Vision Benchmark dataset compared against state of the art algorithms. The results show that the proposed algorithm can detect the traversable regions in a far range efficiently at a high successful rate.
Keywords :
control engineering computing; feature extraction; image classification; image segmentation; learning (artificial intelligence); mobile robots; object detection; robot vision; support vector machines; ISVM; KITTI vision benchmark dataset; Nubot mobile robot; computational complexity; far-range region classification performance; far-range traversable region detection; incremental supported vector machine; near-range LBP features extraction; near-to-far self-supervised incremental learning; outdoor environments; superpixel segmentation; Algorithm design and analysis; Classification algorithms; Feature extraction; Mobile robots; Roads; Support vector machines; Training; SLIC superpixels; incremental SVM; local binary patterns; near-to-far self-supervised learning; traversable region detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6731-5
Type :
conf
DOI :
10.1109/MFI.2014.6997747
Filename :
6997747
Link To Document :
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