Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Exploring unknown environments with mobile robots using coverage maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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This paper presents a methodology for integrating features within the occupancy grid (OG) framework. The OG maps provide a dense representation of the environment. In particular they give information for every range measurement projected onto a grid. However independence assumptions between cells during updates as well as not considering sonar models lead to inconsistent maps, which may also lead the robot to take some decisions which may be unsafe or which may introduce an unnecessary overhead of run-time collision avoidance behaviors. Feature based maps provide more consistent representation by implicitly considering correlation between cells. But they are sparse due to sparseness of features in a typical environment. This paper provides a method for integrating feature based representations within the standard Bayesian framework of OG and provides a dense, more accurate and safe representation than standard OG methods.