Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
International Journal of Computer Vision
Topological mapping for mobile robots using a combination of sonar and vision sensing
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Efficient Computation of the Minimum Hausdorff Distance for Visual Recognition
Efficient Computation of the Minimum Hausdorff Distance for Visual Recognition
An architecture for vision and action
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Action and perception in man-made environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Past work in navigation has worked toward the goal of producing an accurate map of the environment. While no one can deny the usefulness of such a map, the ideal of producing a complete map becomes unrealistic when an agent is faced with performing real tasks. And yet an agent accomplishing recurring tasks should navigate more efficiently as time goes by. We present a system which integrates navigation, planning, and vision. In this view, navigation supports the needs of a larger system as opposed to being a task in its own right. Whereas previous approaches assume an unknown and unstructured environment, we assume a structured environment whose organization is known, but whose specifics are unknown. The system is endowed with a wide range of visual capabilities as well as search plans for informed exploration of a simulated store constructed from real visual data. We demonstrate the agent finding items while mapping the world. In repeatedly retrieving items, the agent's performance improves as the learned map becomes more useful.