Qualitative navigation for mobile robots
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Artificial intelligence and mobile robots: case studies of successful robot systems
Artificial intelligence and mobile robots: case studies of successful robot systems
Integrating topological and metroc maps for mobile robot navigation: a statistical approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Acquisition and Propagation of Spatial Constraints Based on Qualitative Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to AI Robotics
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Map building without localization by dimensionality reduction techniques
Proceedings of the 24th international conference on Machine learning
| Hi-index | 0.00 |
This paper proposes a unique map learning method for mobile robots based on the co-visibility information of objects i.e., the information on whether two objects are visible at the same time or not from the current position. This method first estimates empirical distances among the objects using a simple heuristics - "a pair of objects observed at the same time more frequently is likely to be located more closely together". Then it computes all the coordinates of the objects by multidimensional scaling (MDS) technique. In the latter part of this paper, it is shown that the proposed method is able to learn qualitatively very accurate maps though it uses only such primitive information, and that it is robust against some kinds of object recognition errors.