  Constructing and maintaining a consistent scene model on-the-fly is the core
task for online spatial perception, interpretation, and action. In this paper,
we represent the scene with a Bayesian nonparametric mixture model, seamlessly
describing per-point occupancy status with a continuous probability density
function. Instead of following the conventional data fusion paradigm, we
address the problem of online learning the process how sequential point cloud
data are generated from the scene geometry. An incremental and parallel
inference is performed to update the parameter space in real-time. We
experimentally show that the proposed representation achieves state-of-the-art
accuracy with promising efficiency. The consistent probabilistic formulation
assures a generative model that is adaptive to different sensor
characteristics, and the model complexity can be dynamically adjusted
on-the-fly according to different data scales.
