Y. Liu, R. Emery, D. Chakrabarti, W. Burgard, and S. Thrun
Using EM to Learn 3D Models of
Indoor Environments with Mobile Robots
Proc. of the IEEE International Conference on Machine
Learning (ICML)
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.
Download
Full paper [.ps.gz](3,474,378
bytes)
Bibtex
@InProceedings{Liu01Us,
author = {Liu, Y. and Emery, R. and Chakrabarti,
D. and Burgard, W. and Thrun, S.},
title = {Using {EM} to Learn 3{D}
Models of Indoor Environments with Mobile Robots},
booktitle = ICML,
year = 2001,
note = {to appear}
}