Intelligent neuro-controller for navigation of mobile robot
Proceedings of the International Conference on Advances in Computing, Communication and Control
Appearance-Based SLAM for Mobile Robots
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Appearance-Based SLAM for Mobile Robots
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
GATMO: a generalized approach to tracking movable objects
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
Robotics and Autonomous Systems
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Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.