Planning Routes through uncertain territory
Artificial Intelligence
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Representing and acquiring geographic knowledge
Representing and acquiring geographic knowledge
Representing Knowledge of Large-scale Space
Representing Knowledge of Large-scale Space
A new sense for depth of field
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
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This paper develops a theory for path planning and following using visual landmark recognition for the representation of environmental locations. It encodes local perceptual knowledge in structures called viewframes and orientation regions. Rigorous representations of places as visual events are developed in a uniform framework that smoothly integrates a qualitative version of path planning with inference over traditional metric representations. Paths in the world are represented as sequences of sets of landmarks, viewframes. orientation boundary crossings, and other distinctive visual events. Approximate headings are computed between view frames that have lines of sight to common landmarks. Orientation regions are range-free, topological descriptions of place that are rigorously abstracted from viewframes. They yield a coordinate-free model of visual landmark memory that can also be used for path planning and following. With this approach, a robot can opportunistically observe and execute visually cued "shortcuts".