Environment perception is essential for autonomous mobile robots in human-robot coexisting outdoor environments. One of the important tasks for such intelligent robots is to autonomously detect the traversable region in an unstructured 3D real world. The main drawback of most existing methods is that of high computational complexity. Hence, this paper proposes a binocular vision-based, real-time solution for detecting traversable region in the outdoors. In the proposed method, an appearance model based on multivariate Gaussian is quickly constructed from a sample region in the left image adaptively determined by the vanishing point and dominant borders. Then, a fast, self-supervised segmentation scheme is proposed to classify the traversable and non-traversable regions. The proposed method is evaluated on public datasets as well as a real mobile robot. Implementation on the mobile robot has shown its ability in the real-time navigation applications.
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