Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Landmark Selection for Vision-Based Navigation
IEEE Transactions on Robotics
Pure topological mapping in mobile robotics
IEEE Transactions on Robotics
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In this paper, we consider the exploration of topological environments by a robot with weak sensory capabilities. We assume only that the robot can recognize when it has reached a vertex, and can assign a cyclic ordering to the edges leaving a vertex with reference to the edge it arrived from. Given this limited sensing capability, and without the use of any markers or additional information, we will show that the construction of a topological map is still feasible. This is accomplished through both the exploration strategy which is designed to reveal model inconsistencies and by a search process that maintains a bounded set of believable world models throughout the exploration process. Plausible models are selected through the use of a ranking heuristic function based on the principle of Occam's Razor. We conclude with numerical simulations demonstrating the performance of the algorithm.