The Kalman filter: an introduction to concepts
Autonomous robot vehicles
Position estimation for mobile robots in dynamic environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Deploying Localization Services in Wireless Sensor Networks
ICDCSW '04 Proceedings of the 24th International Conference on Distributed Computing Systems Workshops - W7: EC (ICDCSW'04) - Volume 7
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This paper presents a hybrid localization method designed for environments having the structure of a network (road networks, sewerage networks, underground mines, etc...). The method, which views localization as a problem of state estimation in a switching environment, combines the flexibility and robustness of Markov localization with the accuracy and efficiency of Kalman filtering. This is achieved by letting Markov localization handle the topological aspects of the problem, and Kalman filtering the metric aspects. The two techniques are closely coupled: the Markov model determines the Kalman filters to be initiated, and statistics computed by the Kalman filters are used to define the transition and observation probabilities in the Markov model. This approach has been applied to the problem of localizing a motor vehicle traveling on an urban road network, providing robust and accurate localization at low cost.