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Long-range terrain perception using convolutional neural networks
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DOI：10.1016/j.neucom.2017.09.012.png)
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Autonomous robot navigation in wild environments is still an open problem and relies heavily on accurate terrain perception. Traditional machine learning techniques have achieved good performance for terrain perception; however, most of them require manually designed classifiers, meaning they have a poor generalization ability for learning new unknown environments. In this work, we integrate a deep convolutional neural network (CNN) model with a near-to-far learning strategy to improve the accuracy of terrain segmentation and make it more robust against wild environments. The proposed deep CNN model consists of an encoder and a decoder, which perform downsampling and upsampling for terrain feature extraction, respectively. The near-field terrain information obtained directly from the stereo disparity maps is fed into the CNNs as reference to aid in learning the far-field terrain information. Experimental results on a benchmark dataset demonstrate the effectiveness of the proposed terrain perception method. (c) 2017 Elsevier B.V. All rights reserved.
Keyword:
Terrain perception
Disparity information
Convolutional neural networks
Robot navigation
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6.5
论文数:
2.5W
被引数:
6.5W
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