Long-range terrain perception using convolutional neural networks
Long-range terrain perception using convolutional neural networks
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DOI：
10.1016/j.neucom.2017.09.012
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发表时间：
2018-01
期刊：
影响因子：
6
通讯作者：
Wei Zhang;Qi Chen;W. Zhang;Xuanyu He
中科院分区：
文献类型：
--
作者：
Wei Zhang;Qi Chen;W. Zhang;Xuanyu He
野外环境中的自主机器人导航仍然是一个悬而未决的问题，并且在很大程度上依赖于准确的地形感知。传统的机器学习技术在地形感知方面取得了良好的性能；然而，它们中的大多数需要手动设计的分类器，这意味着它们对于学习新的未知环境的泛化能力很差。在这项工作中，我们将深度卷积神经网络（CNN）模型与近远学习策略相结合，以提高地形分割的准确性，并使其对野生环境更加鲁棒。所提出的深度 CNN 模型由编码器和解码器组成，分别执行下采样和上采样以提取地形特征。直接从立体视差图获得的近场地形信息被馈送到 CNN 中作为参考，以帮助学习远场地形信息。基准数据集上的实验结果证明了所提出的地形感知方法的有效性。
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.