A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Abstract
This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.
Cite
Text
Thrun et al. "A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots." Machine Learning, 1998. doi:10.1023/A:1007436523611Markdown
[Thrun et al. "A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots." Machine Learning, 1998.](https://mlanthology.org/mlj/1998/thrun1998mlj-probabilistic/) doi:10.1023/A:1007436523611BibTeX
@article{thrun1998mlj-probabilistic,
title = {{A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots}},
author = {Thrun, Sebastian and Burgard, Wolfram and Fox, Dieter},
journal = {Machine Learning},
year = {1998},
pages = {29-53},
doi = {10.1023/A:1007436523611},
volume = {31},
url = {https://mlanthology.org/mlj/1998/thrun1998mlj-probabilistic/}
}