PAPER: Using EM to Learn 3D Models with Mobile Robots

By Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti, Wolfram Burgard, and Sebastian Thrun

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 low-complexity 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.

Available for download in

• gzipped Postscript (3,474,378 bytes) and
• low-res PDF (3,043,167 bytes).

@INPROCEEDINGS{Liu01a,
  AUTHOR         = {Liu, Y. and Emery, R. and Chakrabarti, D. and Burgard, W.
                     and Thrun, S.},
  TITLE          = {Using {EM} to Learn {3D} Models with Mobile Robots},
  YEAR           = {2001},
  BOOKTITLE      = {Proceedings of the International Conference on Machine 
                    Learning (ICML)}
}