Using EM to Learn 3D Models of Indoor Environments with Mobile Robots

Abstract

This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a lowcomplexity planar model to 3D data collected by range finders and a panoramic camera. The complexity of the model is determined during model fitting, by incrementally adding and removing surfaces. In a final post-processing step, measurements are converted into polygons and projected onto the surface model where possible. Empirical results obtained with a mobile robot illustrate that high-resolution models can be acquired in reasonable time. 1.

Cite

Text

Liu et al. "Using EM to Learn 3D Models of Indoor Environments with Mobile Robots." International Conference on Machine Learning, 2001.

Markdown

[Liu et al. "Using EM to Learn 3D Models of Indoor Environments with Mobile Robots." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/liu2001icml-using/)

BibTeX

@inproceedings{liu2001icml-using,
  title     = {{Using EM to Learn 3D Models of Indoor Environments with Mobile Robots}},
  author    = {Liu, Yufeng and Emery, Rosemary and Chakrabarti, Deepayan and Burgard, Wolfram and Thrun, Sebastian},
  booktitle = {International Conference on Machine Learning},
  year      = {2001},
  pages     = {329-336},
  url       = {https://mlanthology.org/icml/2001/liu2001icml-using/}
}