Computer vision based wheel sinkage detection for robotic lunar exploration tasks

Guruprasad M. Hegde, Chris Robinson, C. Ye, A. Stroupe, E. Tunstel
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引用次数: 5

Abstract

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.
基于计算机视觉的机器人月球探测任务车轮下沉检测
提出了一种可用于机器人探月任务的车轮下沉检测方法。该方法通过对监控轮土相互作用的摄像机拍摄的轮土图像进行分割,提取机器人轮与月球土壤之间的边界线。将检测到的边界投影到机器人车轮的无土图像上,以确定车轮下沉的深度。分割方法基于图论。它首先将车轮土壤图像聚类到称为超像素的均匀区域,并在超像素上构建图形。然后,它通过使用规范化切割将图划分为段。与现有方法相比,该算法对光照条件、阴影和灰尘(覆盖车轮)具有更强的鲁棒性。通过各种条件下的实验,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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