Evaluation of features through grid association for building a sonar map

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