Topological Mapping with Multiple Visual Manifolds

We address the problem of building topological maps in visual space for robot navigation. The nodes of our topological maps consist of clusters along manifolds, and we propose an unsupervised learning algorithm that automatically constructs these manifolds the user need only specify the desired number of clusters and the minimum number of images per cluster. This spectral clustering like framework allows each cluster to optimize a separate set of clustering parameters, and we demonstrate empirically that this flexibility can significantly improve clustering results. We further propose a framework for servoing the robot in our manifold space, which would allow the robot to navigate from any point on one manifold (topological node) to any specified point on a second manifold. Finally, we present experimental results on indoor and outdoor image sequences demonstrating the efficacy of the proposed algorithm.