Physically Based Simulation Model for Acoustic Sensor Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Introduction to AI Robotics
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
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
Directional Processing of Ultrasonic Arc Maps and its Comparison with Existing Techniques
International Journal of Robotics Research
Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM
International Journal of Robotics Research
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
Approximate robotic mapping from sonar data by modeling perceptions with antonyms
Information Sciences: an International Journal
Probabilistic multi-level maps from LIDAR data
International Journal of Robotics Research
Topological localization with kidnap recovery using sonar grid map matching in a home environment
Robotics and Computer-Integrated Manufacturing
Evolutionary computation for intelligent self-localization in multiple mobile robots based on SLAM
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
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In this paper, we address the problem of building a grid map using cheap sonar sensors, i.e., 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, which is called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood (ML) 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.