Learned geometric features of 3D range data for human and tree recognition

A new method of obtaining geometric features of three-dimensional range data for human and tree recognition in an off-road environment is described. The learning algorithm AdaBoost is used to select a set of discriminative features from a very large set of potential geometric features. The proposed geometric feature can be considered as a generalisation of the geometric feature used in previous studies. The experimental results for human and tree recognition show that the proposed method outperforms the other methods.