Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Occupancy grids building by sonar and mobile robot
Robotics and Autonomous Systems
High-Resolution Ultrasonic Environment Imaging
IEEE Transactions on Robotics
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we address the problem of building a grid map using cheap sonar sensors, that is, the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict-evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors, and does not require adjustment of parameters for various environments.