View-based localization in outdoor environments based on support vector learning

This paper describes a view-based localization method using support vector machines in outdoor environments. We have been developing a two-phase vision-based navigation method. In the training phase, the robot acquires image sequences along the desired route and automatically learns the route visually. In the subsequent autonomous navigation phase, the robot moves by localizing itself based on the comparison between input images and the learned route representation. Our previous localization method uses an object recognition method which is robust to changes of weather and the seasons; however it has many parameters and threshold values to be manually adjusted. This paper, therefore, applies a support vector machine (SVM) algorithm to this object recognition problem. SVM is also applied to discriminating locations based on the recognition results. This two-stage SVM-based localization approach exhibits a satisfactory performance for real outdoor image data without any manual adjustment of parameters and threshold values.