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Visual navigation is an important technology for Autonomous Guided Vehicles and road detection is the essential prerequisite. It is a challenging problem to distinguish the road or its boundaries in unstructured environments for the lack of discernible features. A self-supervised road detection method is proposed in this paper. Vanishing point is first detected using only four Gabor filters to precisely estimate the local dominant orientation at each pixel location, and an adaptive soft voting scheme is used to prevent tending to favor points that are high in the image. Training area is defined with vanishing point, which color feature is used for building self-supervised learning models to describe the road segment. Road patches are selected by measuring the roadness score. Experimental results have shown the efficiency of the method in terms of detection result and time saving.

A Self-Supervised Road Detection Method Based on Gabor Filter