A bayesian approach to learning 3d representations of dynamic environments

We propose a novel probabilistic approach to learning spatial representations of dynamic environments from 3D laser range measurements. Whilst most of the previous techniques developed in robotics address this problem by computationally expensive tracking frameworks, our method performs in real-time even in the presence of large amounts of dynamic objects. The computer vision community has provided comparable methods for learning foreground activity patterns in images. However, these methods generally do not account well for the uncertainty involved in the sensing process. In this paper, we show that the problem of detecting occurrences of non-stationary objects in range readings can be solved online under the assumption of a consistent Bayesian framework. Whilst the model underlying our framework naturally scales with the complexity and the noise characteristics of the environment, all parameters involved in the detection process obey a clean probabilistic interpretation. When applied to real-world urban settings, the results produced by our approach appear promising and may directly be applied to solve map building, localization, or robot navigation problems.