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ROVER TRAVERSABILITY ASSESSMENT VIA VISUAL SENSING OF SPATIAL AND TEXTURAL TERRAIN IMAGE FEATURES

机译:通过空间和纹理地形图像特征的视觉感知,对流动性进行评估

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Visual sensing techniques are presented tor enhancing rover traversability assessment on planetary surfaces. Geometric information from stereo image range data is used to identify salient terrain features such as rocks, and that information, along with terrain image appearances, is associated with rover traversability. Performance of rule-based, neural network, and fuzzy logic methods for classifying Mars terrain is evaluated revealing superior capabilities of the latter two. Bayesian, Parzen, and k-Nearest Neighbor decision fusion models are considered to improve classification, revealing slightly better performance of the former. Terrain classifier path planning applications are verified experimentally using a mobile robot in mock terrain.
机译:提出了视觉传感技术,以增强行星表面的流动性。来自立体图像范围数据的几何信息用于识别突出的地形特征(例如岩石),并且该信息以及地形图像的外观与流动站的可穿越性相关。对基于规则的神经网络和模糊逻辑方法对火星地形进行分类的性能进行了评估,揭示了后两种方法的出色功能。贝叶斯,Parzen和k最近邻居决策融合模型被认为可以改善分类,从而揭示前者的性能略好。使用移动机器人在模拟地形中对地形分类器路径规划应用进行了实验验证。

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