A boosting approach for object classification in biosonar based robot navigation

This paper addresses the problem of object classification in a biosonar based mobile robot in a natural environment using a boosting method. We present an algorithm based on gradient boosting for biosanar-based robots that recognize different objects such as different trees via reflected sonar echoes. Gradient boosting is a machine learning approach, that builds one strong classifier from many base learners. We present two kinds of base learners for the gradient boosting: ordinary least squares (OLS) and kernel-based base learners. Compared with our previous works, in which we presented a time resolved spectrum kernel to extract the similarities between echoes, we get more efficient and accurate results with the newly proposed boosting method. We compare the methods in terms of sensitivity, specificity, accuracy and Matthew´s correlation coefficient and also the runtime of training and testing.