Topological Mapping from Image Sequences

An autonomous agent should be able to traverse a new environment and construct a topological representation of what it has seen. We present two new semi-supervised learning techniques which allow us to segment extended sensor (image) sequences into a topological map by clustering on low-dimensional manifolds in sensor space. The general approach is based on outlier detection in manifold space, closely related to spectral clustering. The first technique fixes the s parameter of the affinity matrix, the second allows each cluster to optimize for a different s. In both cases manifold clusters can be associated with the user’s conceptual map by labelling one image per cluster. We demonstrate these techniques for indoor and outdoor sequences.