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Visual robot navigation within large-scale, semi-structured environmentsdeals with various challenges such as computation intensive path planningalgorithms or insufficient knowledge about traversable spaces. Moreover, manystate-of-the-art navigation approaches only operate locally instead of gaininga more conceptual understanding of the planning objective. This limits thecomplexity of tasks a robot can accomplish and makes it harder to deal withuncertainties that are present in the context of real-time roboticsapplications. In this work, we present Topomap, a framework which simplifiesthe navigation task by providing a map to the robot which is tailored for pathplanning use. This novel approach transforms a sparse feature-based map from avisual Simultaneous Localization And Mapping (SLAM) system into athree-dimensional topological map. This is done in two steps. First, we extractoccupancy information directly from the noisy sparse point cloud. Then, wecreate a set of convex free-space clusters, which are the vertices of thetopological map. We show that this representation improves the efficiency ofglobal planning, and we provide a complete derivation of our algorithm.Planning experiments on real world datasets demonstrate that we achieve similarperformance as RRT* with significantly lower computation times and storagerequirements. Finally, we test our algorithm on a mobile robotic platform toprove its advantages.

Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps