Algorithm Fusion for Feature Extraction and Map Construction From SONAR Data

Feature extraction is an important aspect of simultaneous localization and mapping, a process, in which a mobile platform is used to create a map of an unknown environment, while simultaneously locating the platform's own position within the constructed map or environment. Geometric shapes or features, such as lines, circles, and interior and exterior corners, are determined as a part of this process, and these features may be used as landmarks. In this paper, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been put together by combining the SONAR salient feature extraction algorithm and the triangulation Hough-based fusion with the point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared with the maps obtained with existing feature extraction algorithms. Based on the results, it is suggested that the proposed algorithm can be considered as an option for the data obtained from SONAR sensors in environments, where the other forms of sensing are not viable.