Physically Based Simulation Model for Acoustic Sensor Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differentiating Sonar Reflections from Corners and Planes by Employing an Intelligent Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic map building for an autonomous mobile robot
International Journal of Robotics Research
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Mobile robot sonar for target localization and classification
International Journal of Robotics Research
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
A Physical Model-Based Analysis of Heterogeneous Environments Using Sonar-ENDURA Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpretation of Ultrasonic Readings for Autonomous Robot Localization
Journal of Intelligent and Robotic Systems
Map estimation using GPS-equipped mobile wireless nodes
Pervasive and Mobile Computing
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We present a feature-based probabilistic map building algorithm which directly utilizes time and amplitude information of sonar in indoor environments. Utilizing additional amplitude-of-signal (AOS) obtained concurrently with time-of-flight (TOF), the amount of inclination of target can be directly calculated from a single echo, and the number of measurements can be greatly reduced with result similar to dense scanning. A set of target groups (set of hypothesized targets originated from one measurement) is used and refined by each measurement using an extended Kalman filter and Bayesian conditional probability. Experimental results in a real indoor environment are presented to show the validity of our algorithm.