Simultaneous localization and uncertainty reduction on maps (SLURM): Ear based exploration

Efficient exploration and accurate mapping are two conflicting goals. Efficient exploration requires minimizing traversal of previously mapped territory, accurate mapping necessitates that the robot goes through previously mapped areas to reduce the accumulated uncertainty. This problem has many parallels with the exploration versus exploitation problem. In this paper a new algorithm is proposed that explicitly aims to facilitate loop closure in a systematic way. The problem of localizing a camera sensor network by employing a mobile robot will be used to demonstrate the effect that different parameters of the ear-based exploration strategy have on the speed of exploration and the accumulated uncertainty. Simulation results using a realistic noise model are presented for different environments.