On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Tracking and data association
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
An Behavior-based Robotics
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Occupancy grids: a probabilistic framework for robot perception and navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Simultaneous Localization, Mapping and Moving Object Tracking
International Journal of Robotics Research
New lower bound techniques for robot motion planning problems
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Mobile robot mapping and localization in non-static environments
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Metric-based iterative closest point scan matching for sensor displacement estimation
IEEE Transactions on Robotics
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Synchronous EEG brain-actuated wheelchair with automated navigation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Human brain-teleoperated robot between remote places
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information with a sensor-based motion planning system. The contribution in the modeling aspect is a formulation of the detection and tracking of mobile objects and the mapping of the static structure in such a way that the nature (static/dynamic) of the observations is included in the estimation process. The algorithm provides a set of filters tracking the moving objects and a local map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based motion planning system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.