Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
ACM Computing Surveys (CSUR)
Bayesian modeling of human concept learning
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IEEE Transactions on Knowledge and Data Engineering
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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Hierarchical Part-Based Visual Object Categorization
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
Supervised semantic labeling of places using information extracted from sensor data
Robotics and Autonomous Systems
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Learning search heuristics for finding objects in structured environments
Robotics and Autonomous Systems
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Holography map for home robot: an object-oriented approach
Intelligent Service Robotics
Online semantic mapping of urban environments
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
KnowRob: A knowledge processing infrastructure for cognition-enabled robots
International Journal of Robotics Research
Indoor scene recognition by a mobile robot through adaptive object detection
Robotics and Autonomous Systems
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The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, this work attempts to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, it details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Several algorithms, from naive ones using only object category presence to more sophisticated ones using both objects and relationships, are proposed. Both learning and inference use the information encoded in the underlying representation-objects and relative spatial information between them. The approaches are based on learning from exemplars, clustering and the use of Bayesian network classifiers. The approaches are generative. Further, even though they are based on learning from exemplars, they are not ontology specific; i.e. they do not assume the use of any particular ontology. The presented algorithms rely on a robots inherent high-level feature extraction capability (object recognition and structural element extraction) capability to actually form concept models and infer them. Thus, this report presents methods that could enable a robot to to link sensory information to increasingly abstract concepts (spatial constructs). Such a conceptualization and the representation that results thereof would enable robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of this work is-conceptualization and classification for representation and spatial cognition.