Object Classification with Range and Reflectance Data from a Single Laser Scanner

This paper presents a new object classification technique for 3D point cloud data acquired with a laser scanner. In general, it is not straightforward to distinguish objects that have similar 3D structures but belong to different categories based only on the range data. To tackle this issue, we focus on laser reflectance obtained as a side product of range measurement by a laser scanner. Since laser reflectance contains appearance information, the proposed method classifies objects based on not only geometrical features in range data but also appearance features in reflectance data, both of which are acquired by a single laser scanner. Furthermore, we extend the conventional Histogram of Oriented Gradients (HOG) so that it couples geometrical and appearance information more tightly. Experiments show the proposed technique combining geometrical and appearance information outperforms conventional techniques.