A CNN Based Vision-Proprioception Fusion Method for Robust UGV Terrain Classification
A CNN Based Vision-Proprioception Fusion Method for Robust UGV Terrain Classification
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DOI：
10.1109/lra.2021.3101866
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发表时间：
2021-10-01
影响因子：
5.2
通讯作者：
Norris, William R.
中科院分区：
文献类型：
--
作者：
Chen, Yu;Rastogi, Chirag;Norris, William R.
地面车辆识别地形类型和特征的能力有助于提供更准确的定位和信息丰富的地图解决方案。以前的研究表明，基于监测车轮-地形相互作用的本体感知传感器，可以对地形类型进行分类。然而，大多数方法只有在施加非常严格的运动限制时才能起到很好的作用，包括以恒速在直线上行驶，这使得它们很难部署在现实世界的野外机器人任务中。为了解除这一限制，本文提出了一种快速、紧凑、运动稳健、基于本体感觉的地形分类方法。该方法使用普通车载UGV传感器和一维卷积神经网络(CNN)模型。将该模型与基于视觉的细胞神经网络进行融合，进一步提高了模型的精度。实验结果表明，最终的融合模型具有很强的鲁棒性和较强的性能，在不同的光照条件和运动动作下，准确率都在93%以上。
The ability for ground vehicles to identify terrain types and characteristics can help provide more accurate localization and information-rich mapping solutions. Previous studies have shown the possibility of classifying terrain types based on proprioceptive sensors that monitor wheel-terrain interactions. However, most methods only work well when very strict motion restrictions are imposed including driving in a straight path with constant speed, making them difficult to be deployed on real-world field robotic missions. To lift this restriction, this letter proposes a fast, compact, and motion-robust, proprioception-based terrain classification method. This method uses common on-board UGV sensors and a 1D Convolutional Neural Network (CNN) model. The accuracy of this model was further improved by fusing it with a vision-based CNN that made classification based on the appearance of terrain. Experimental results indicated the final fusion models were highly robust with strong performance, with over 93% accuracy, under various lighting conditions and motion maneuvers.