This paper addresses a new feature map building method that can minimizes the appearance of phantom features by using only sparse sonar data. The approach is composed of extraction of features and building a probability grid map using only the footprint of sparse sonar data, estimation of position uncertainty of the feature, and evaluation of the reliable features. A virtual circle association frame model has been developed, which associates two sonar footprints into a virtual circle frame. Using this model, the geometric primitives such as lines, points, and arc features are separately extracted. While extracting the features, a grid map is also built using the orientation probability approach. The position uncertainty of each extracted feature is, then, estimated by considering both the position uncertainty of the robot and the measurement uncertainty of the sonar sensor. Finally, the reliable features among all extracted ones are evaluated from grid association method. The proposed methods have been tested in a real home environment with a mobile robot

A new feature based map building model that uses only the footprints of sparse sonar data has been developed and implemented. An arc feature association model was developed, which associates two sonar footprints into an arc feature. The association model provides information on the regions of center points of the arc feature. The information makes it possible to decide weather the two sonar footprints are associated with a line, point or arc. Lines, points, or arc features are extracted from more than two independent sonar footprints using the arc feature association model. We also propose a method that estimates the position uncertainties of the extracted features by considering both the pose uncertainty of the robot and the measurement uncertainty of the sonar sensor. This pose uncertainty is also used in the arc feature association process of the sonar footprints. The proposed method has been tested in a real home environment with a mobile robot.

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.

Presents a technique for relocation of a mobile robot using sonar data. Relocation is the process of determining the position and orientation of a mobile robot with respect to the global reference frame in situations where no a priori estimate of the robot's location is available. A two dimensional model of the environment is employed consisting of planes, corners, edges, and cylinders. A physically based sonar sensor model is used to characterize the geometric constraints provided by echolocation measurements of different types of objects. The sensor model makes it possible to employ individual range returns as data features in a constraint-based search to determine the robot's position. To increase the efficiency of search, a hypothesize and verify technique is employed in which position of the robot are calculated from all possible combinations of two range returns that satisfy the measurement model. Accurate relocation is demonstrated from sparse sonar data in the presence of unmodeled objects.

A method for solving the specular reflection problem of sonar systems has been developed and implemented. This method, the specular reflection probability method, permits the robot to construct a high-quality probability map of an environment composed of specular surfaces. The method uses two parameters, the range confidence factor (RCF) and orientation probability. The RCF is the measure of confidence in the returning range from a sensor under a reflective environment, and the factor will have low values for long range information and vice versa. Orientation probability represents the surface orientation of an object. Bayesian reasoning is used to update the orientation probability from the range readings of the sensor.>