Terrain Classification and Classifier Fusion for Planetary Exploration Rovers

I. Halatci, Christopher A. Brooks, K. Iagnemma
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引用次数: 80

Abstract

Knowledge of the physical properties of terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Two classification algorithms for color, texture, and range features are presented based on maximum likelihood estimation and support vector machines. In addition, a classification method based on vibration features derived from rover wheel-terrain interaction is briefly described. Two techniques for merging the results of these "low-level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performance of these algorithms is studied using images from NASA's mars exploration rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual and tactile features.
行星探测车地形分类与分类器融合
行星探测车周围地形的物理特性知识可以用来使探测车系统充分利用其机动能力。本文研究了火星和类火星环境下行星漫游者的多传感器地形分类。提出了两种基于极大似然估计和支持向量机的颜色、纹理和距离特征分类算法。此外,还简要介绍了一种基于漫游车车轮-地形相互作用产生的振动特征的分类方法。提出了两种合并这些“低级”分类器结果的技术,它们依赖于贝叶斯融合和元分类器融合。这些算法的性能是通过NASA火星探测漫游车任务的图像和在火星模拟地形中运行的四轮试验台漫游车上的实验来研究的。结果表明,通过视觉和触觉特征的分类器融合,可以实现准确的地形分类。
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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