Online Classification in 3D Urban Datasets Based on Hierarchical Detection

One of the most significant problems in the area of 3D range image processing is that of segmentation and classification from 3D laser range data, especially in real-time. In this work we introduce a novel multi-layer approach to the classification of 3D laser scan data. In particular, we build a hierarchical framework of online detection and identification procedures drawn from sequential analysis namely the CUSUM (Cumulative Sum) and SPRT (Sequential Probability Ratio Test), both of which are low complexity algorithms. Each layer of algorithms builds upon the decisions made at the previous stage thus providing a robust framework of online decision making. In our new framework we are not only able to classify in coarse classes such as vertical, horizontal and/or vegetation but to also identify objects characterized by more subtle or gradual changes such as curbs or steps. Moreover, our new multi-layer approach combines information across scan lines and results in more accurate decision making. We perform experiments in complex urban scenes and provide quantitative results.