In this paper we describe a monocular vision based method to learn navigable terrain for autonomous rover navigation. A self-supervised learning mechanism adjusts the surface appearance model using monocular image sequences. We propose a computationally inexpensive approach to labeling of the ground plane pixels using a reactive pre-filter. An active window, centered at the current position of the robot, implements the pre-filter based on majority voting. The selection criterion employs only a comparison, addition, and bit shifts using integer arithmetic. Hence, the scoring mechanism is directly implementable using an integer data path, associated with a reduced area overhead for resource constrained rover applications. The labeled navigable pixels are used as training data. The learning algorithm uses a mixture of Gaussians to model the terrain. We present empirical results on heterogeneous obstacle field configurations and varying terrain types.
Computationally inexpensive labeling of appearance based navigable terrain for autonomous rovers
2013-04-01
3021109 byte
Conference paper
Electronic Resource
English
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