Tracking and data association
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anchoring Symbols to Sensor Data: Preliminary Report
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Recovery planning for ambiguous cases in perceptual anchoring
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Maintaining coherent perceptual information using anchoring
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multiple hypothesis tracking in microscopy images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Scene parsing using a prior world model
International Journal of Robotics Research
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In order to successfully perform typical household tasks such as manipulation or navigation, domestic robots need an accurate description of the world they are operating in. Creating and maintaining such a description, in this work referred to as world model, is a non-trivial task in a domestic environment that typically has a high number of objects, and is unstructured and dynamically changing. This work introduces probabilistic multiple hypothesis anchoring to create and maintain a semantically rich world model using probabilistic anchoring. Multiple hypothesis tracking-based data association is included to be able to deal with ambiguous scenarios. Multiple model tracking is included to be able to easily incorporate different kinds of prior knowledge.