Parsing outdoor scenes from streamed 3D laser data using online clustering and incremental belief updates

In this paper, we address the problem of continually parsing a stream of 3D point cloud data acquired from a laser sensor mounted on a road vehicle. We leverage an online star clustering algorithm coupled with an incremental belief update in an evolving undirected graphical model. The fusion of these techniques allows the robot to parse streamed data and to continually improve its understanding of the world. The core competency produced is an ability to infer object classes from similarities based on appearance and shape features, and to concurrently combine that with a spatial smoothing algorithm incorporating geometric consistency. This formulation of feature-space star clustering modulating the potentials of a spatial graphical model is entirely novel. In our method, the two sources of information: feature similarity and geometrical consistency are fed continually into the system, improving the belief over the class distributions as new data arrives. The algorithm obviates the need for hand-labeled training data and makes no a-priori assumptions on the number or characteristics of object categories. Rather, they are learnt incrementally over time from streamed input data. In experiments performed on real 3D laser data from an outdoor scene, we show that our approach is capable of obtaining an ever-improving unsupervised scene categorization.