Sequential Classification in Point Clouds of Urban Scenes

Laser range scanners have now the ability to acquire millions of 3D points of highly detailed and geometrically complex urban sites, opening new avenues of exploration in modeling urban environments. In the traditional modeling pipeline, range scans are processed off-line after acquisition. The slow sequential acquisition though is a bottleneck. The goal of our work is to alleviate this bottleneck, by exploiting the sequential nature of the data acquisition process. We have developed novel online algorithms, never before used in laser range scanning, that perform data classification on-the-fly as data is being acquired. These algorithms are extremely efficient, and can be potentially integrated with the scanner’s hardware, rendering a sensor that not only acquires but also intelligently processes and classifies the scene points. This sensor, armed with the proposed algorithms, can classify 3D points in real-time as being in vegetation vs. non-vegetation regions, or in horizontal vs. vertical regions. The former classification is possible by the implementation of sequential algorithms through a hidden Markov model (HMM) formulation, and the latter through the use of a combination of cleverly designed sequential detection algorithms. We envision an arsenal of algorithms of this type to be developed in the future.