Learning Topological Maps: An Alternative Approach
Abstract
Our goal is autonomous real-time control of a mobile robot. In this paper we want to show a possibility to learn topological maps of a large-scale indoor environment autonomously. In the literature there are two paradigms how to store information on the environment of a robot: as a grid-based (geometric) or as a topological map. While grid-based maps are considerably easy to learn and maintain, topological maps are quite compact and facilitate fast motionplanning.
Cite
Text
Bücken and Thrun. "Learning Topological Maps: An Alternative Approach." AAAI Conference on Artificial Intelligence, 1996.Markdown
[Bücken and Thrun. "Learning Topological Maps: An Alternative Approach." AAAI Conference on Artificial Intelligence, 1996.](https://mlanthology.org/aaai/1996/bucken1996aaai-learning/)BibTeX
@inproceedings{bucken1996aaai-learning,
title = {{Learning Topological Maps: An Alternative Approach}},
author = {Bücken, Arno and Thrun, Sebastian},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1996},
pages = {1380},
url = {https://mlanthology.org/aaai/1996/bucken1996aaai-learning/}
}