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Computer vision based wheel sinkage detection for robotic lunar exploration tasks

机译:基于计算机视觉的轮沉探测,用于机器人探月任务

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This paper presents a wheel sinkage detection method that may be used in robotic lunar exploration tasks. The method extracts the boundary line between a robot wheel and lunar soil by segmenting the wheel-soil images captured from a video camera that monitors wheel-soil interaction. The detected boundary is projected onto the soil-free image of the robot wheel to determine the depth of wheel sinkage. The segmentation method is based on graph theory. It first clusters a wheel-soil image into homogeneous regions called superpixels and constructs a graph on the superpixels. It then partitions the graph into segments by using normalized cuts. Compared with the existing methods, the proposed algorithm is more robust to illumination condition, shadows and dust (covering the wheel). The method's efficacy has been validated by experiments under various conditions.
机译:本文提出了一种可在机器人登月探测任务中使用的车轮沉没检测方法。该方法通过分割从监视车轮-土壤相互作用的摄像机捕获的车轮-土壤图像来提取机器人车轮和月球土壤之间的边界线。将检测到的边界投影到机械手车轮的无土图像上,以确定车轮下沉的深度。分割方法是基于图论的。它首先将车轮土壤图像聚类到称为超像素的同质区域,然后在超像素上构建图。然后,使用归一化的切割将图划分为多个段。与现有方法相比,所提出的算法在光照条件,阴影和灰尘(覆盖车轮)方面具有更强的鲁棒性。通过在各种条件下的实验验证了该方法的有效性。

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