A vision-based detection algorithm for unmarked road

A new detection method for unstructured road based on robot's vision is proposed to improve the effectiveness of road detection in complex environment. In this article, the OTSU, an auto-adapted threshold searching algorithm, is mainly used to classify the road images. Meanwhile, to solve the problems of misclassification in complex environment, the OTSU will be used the second time to subdivide. And multiple scene templates are built combining road referring window (RRW). Then, multi-dimensional features are chosen for region reorganizing according to those templates to obtain the optimal classification. At last, the classifying results are merged by referring RRW to extract the final road region accurately. This algorithm shows good self-adaptive ability and only needs little priori knowledge. It is also robust against noises, shadows and illumination variations and shows good real-time performance. It has been tested on real robot and performed well in real road environment.