Compression of topological models and localization using the global appearance of visual information
In this work, a clustering approach to obtain compact topological models of an environment is developed and evaluated. The usefulness of these models is tested by studying their utility to solve the robot localization problem subsequently. Omnidirectional visual information and global appearance descriptors are used both to create and compress the models and to estimate the position of the robot. Comparing to the methods based on the extraction and description of landmarks, global appearance approaches permit building models that can be handled and interpreted more intuitively and using relatively straightforward algorithms to estimate the position of the robot. The proposed algorithms are tested with a set of panoramic images captured with a catadioptric vision sensor in a large environment under real working conditions. The results show that it is possible to compress substantially the visual information contained in topological models to arrive to a balance between the computational cost and the accuracy of the localization process.