Scene Analysis for Mobile Robot Based on Multi-Sonar-Ranger Data

Abstract

The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A new scene analysis method using kernel principal component analysis (PCA) for mobile robot based on multi-sonar-ranger data is put forward. The principle of classification by principal component analysis (PCA), Kernel-PCA, and the BP neural network approach to extract the largest k eigenvectors are introduced briefly. Next PCA, Kernel-PCA and the BP neural network methods are applied in the corridor scene analysis and classification for the mobile robots based on sonar data. At last the experimental results using PCA, Kernel-PCA and the BP neural network are compared and such conclusions are drawn: in common corridor scene classification, the Kernel-PCA method has advantage over the ordinary PCA, and the BP neural network approach can also get satisfactory result.