Reward-based learning of optimal cue integration in audio and visual depth estimation

Abstract

Many real-world applications in robotics have to deal with imprecisions and noise when using only a single information source for computation. Therefore making use of additional cues or sensors is often the method of choice. One examples considered in this paper is depth estimation where multiple visual and auditory cues can be combined to increase… (More)

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Cite this paper

@article{Karaoguz2011RewardbasedLO, title={Reward-based learning of optimal cue integration in audio and visual depth estimation}, author={Cem Karaoguz and Thomas H. Weisswange and Tobias Rodemann and Britta Wrede and Constantin A. Rothkopf}, journal={2011 15th International Conference on Advanced Robotics (ICAR)}, year={2011}, pages={389-395} }