Mobile Robot Localization Using Sonar
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Sensor fusion in certainty grids for mobile robots
Sensor devices and systems for robotics
Autonomous robot vehicles
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Building a Sonar Map in a Specular Environment Using a Single Mobile Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating topological and metroc maps for mobile robot navigation: a statistical approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Voronoi graph matching for robot localization and mapping
Transactions on computational science IX
Voronoi graph matching for robot localization and mapping
Transactions on computational science IX
Linear-time robot localization and pose tracking using matching signatures
Robotics and Autonomous Systems
Topological localization with kidnap recovery using sonar grid map matching in a home environment
Robotics and Computer-Integrated Manufacturing
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This paper presents a method for relocation of a mobile robot using sonar data. The process of determining the pose of a mobile robot with respect to a global reference frame in situations where no a priori estimate of the robot's location is available is cast as a problem of searching for correspondences between measurements and an a priori map of the environment. A physically-based sonar sensor model is used to characterize the geometric constraints provided by echolocation measurements of different types of objects. Individual range returns are used as data features in a constraint-based search to determine the robot's position. A hypothesize and test technique is employed in which positions of the robot are calculated from all possible combinations of two range returns that satisfy the measurement model. The algorithm determines the positions which provide the best match between the range returns and the environment model. The performance of the approach is demonstrated using data from both a single scanning Polaroid sonar and from a ring of Polaroid sonar sensors.