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Dense Semantic Stereo Labelling Architecture for In-Campus Navigation

Interest on autonomous vehicles has rapidly increased in the last few years, due to recent advances in the field and the appearance of semi-autonomous solutions in the market. In order to reach fully autonomous navigation, a precise understanding of the vehicle surroundings is required. This paper presents a novel ROSbased architecture for stereo-vision-based semantic scene labelling. The objective is to provide the necessary information to a path planner in order to perform autonomous navigation around the university campus. The output of the algorithm contains the classification of the obstacles in the scene into four different categories: traversable areas, garden, static obstacles, and pedestrians. Validation of the labelling method is accomplished by means of a hand-labelled ground truth, generated from a stereo sequence captured in the university campus. The experimental results show the high performance of the proposed approach.