The number of small semispaces of a finite set of points in the plane
Journal of Combinatorial Theory Series A
Towards a computational theory of cognitive maps
Artificial Intelligence
Qualitative navigation for mobile robots
Artificial Intelligence
Qualitative spatial reasoning: the CLOCK project
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Qualitative kinematics of linkages
Recent advances in qualitative physics
Multiagent systems
A new approach to cyclic ordering of 2D orientations using ternary relation algebras
Artificial Intelligence
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Acquisition of Qualitative Spatial Representation by Visual Observation
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Using Orientation Information for Qualitative Spatial Reasoning
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
Distributed vision system: a perceptual information infrastructure for robot navigation
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Subjective local maps for hybrid metric-topological SLAM
Robotics and Autonomous Systems
Qualitative map learning based on co-visibility of objects
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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In robot navigation, one of the important and fundamental issues is to find positions of landmarks or vision sensors located around the robot. This paper proposes a method for reconstructing qualitative positions of multiple vision sensors from qualitative information observed by the vision sensors, i.e., motion directions of moving objects. In order to directly acquire the qualitative positions of points, the method proposed in this paper iterates the following steps: 1) observing motion directions (left or right) of moving objects with the vision sensors, 2) classifying the vision sensors into spatially classified pairs based on the motion directions, 3) acquiring three point constraints, and 4) propagating the constraints. Compared with the previous methods, which reconstruct the environment structure from quantitative measurements and acquire qualitative representations by abstracting it, this paper focuses on how to acquire qualitative positions of landmarks from low-level, simple, and reliable information (that is, 驴qualitative驴). The method has been evaluated with simulations and also verified with observation errors.