Mobile robots do not adequately represent the objects in their environment; this weakness hinders a robot's ability to utilize past experience. In this paper, we describe a simple and novel approach to create object shape models from range sensors. We propose an algorithm that defines angular constraints between multiple sensor scans of an object. These constraints are used to align the scans, creating a maximally coherent object shape model. We demonstrate the utility of this shape model, consisting of scans and poses, for both object recognition and localization. The results are accurate to within sensor precision.

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In IEEE International Conference on Robotics and Automaton 2006.

@inproceedings{modayil:ieee-raa06, title={Autonomous Shape Model Learning for Object Localization and Recognition}, author={Joseph Modayil and Benjamin Kuipers}, booktitle={IEEE International Conference on Robotics and Automaton}, url="http://www.cs.utexas.edu/users/ai-lab?modayil:ieee-raa06", year={2006} }