Qualitative navigation for mobile robots
Artificial Intelligence
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Sequence alignment with tandem duplication
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Combining kalman filtering and Markov localization in network-like environments
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Fusion of laser and visual data for robot motion planning and collision avoidance
Machine Vision and Applications
Tracking of facial features to support human-robot interaction
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Fuzzy uncertainty modeling for grid based localization of mobile robots
International Journal of Approximate Reasoning
How the Location of the Range Sensor Affects EKF-based Localization
Journal of Intelligent and Robotic Systems
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In this paper we address one of the most important issues for autonomous mobile robots, namely their ability to localize themselves safely and reliably within their environments. We propose a probabilistic framework for modelling the robot's state and sensory information based on a Switching State-Space Model. The proposed framework generalizes two of the most successful probabilistic model families currently used for this purpose: the Kalman filter Linear models and the Hidden Markov Models. The proposed model combines the advantages of both models, relaxing at the same time inherent assumptions made individually in each of these existing models.