Learning-based Visual Localization Using Formal Concept Lattices

We present here a new methodology to perform active visual localization in the context of autonomous mobile robotics. The robot is endowed with a topological map of its environment. During the learning phase, the robot takes a lot of pictures from the environment; each picture is labelled by its origin place in the topological map. After the learning phase, the robot is supposed to locate itself in the learnt environment using the visual sensor. Since the discriminating information is sparse, the usual supervised classification techniques as neural networks are not sufficient to perform efficiently this task. Therefore, we propose to use a symbolic learning approach, the "formal concept analysis". The relevant information is gathered into one concept lattice. A formal classification rule is proposed to achieve localization on the topological map. In order to improve the response rate of the decision process, the original formal landmark set is extended to plausible landmarks for a given confidence level. Experimental results in a structured environment support this approach. Perspectives for implementing active strategy to look for visual information and to improve on-line learning and localization process are presented in the final discussion