Jorge Davila Chacon - Visual-Topological Mapping: An approach for indoor robot navigation

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Using visual features for robotic navigation in natural and changing environments remains to be an unsolved problem. This paper describes the use of nearest neighbor algorithm on salient visual features for indoor mapping. The mapping procedure consists on defining an arbitrary number of vector fields -or paths- in a domestic environment, pointing towards a set of target locations. From these specified paths, the robot constantly estimates its most probable location, adjusts its orientation and moves forward a predefined distance, in order to converge to a target location. A Speeded Up Robust Features (SURF) implementation was used for object detection and scene recognition. For every location in a path, the perceived scene was represented with the set of detected objects, and their particular geometric properties relative to the robot's body. A first experiment confirmed that the deviation from the trained paths -due to odometric error- decreased as the number of trained intermediate-locations increased. A second experiment showed promising results when testing the effectiveness of nearest neighbor search for estimating the robot location. Finally, in a third experiment was observed a satisfactory convergence rate of the robot towards predefined target locations, under real world circumstances.