A Bayesian Approach to Learning 3D Representations of Dynamic Environments

  • Ralf Kästner
  • Nikolas Engelhard
  • Rudolph Triebel
  • Roland Siegwart
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)

Abstract

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.

Keywords

Ground Truth Mobile Robot Gaussian Mixture Model Dynamic Environment Dynamic Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Ralf Kästner
    • 1
  • Nikolas Engelhard
    • 2
  • Rudolph Triebel
    • 1
  • Roland Siegwart
    • 1
  1. 1.Autonomous Systems LabETH ZurichZurichSwitzerland
  2. 2.University of FreiburgFreiburgGermany

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