Collaborative Exploration for Map Construction
Ioannis Rekleitis, Robert Sim, Gregory Dudek, Evangelos Milios
In IEEE International Symposium on Computational Intelligence in Robotics and Automation 2001.
[22 citations]Abstract
Abstract: We consider the problem of map learning while maintaining ground-truth pose estimates. Map learning is important in tasks that require a model of the environment or some of its features. As a robot collects data, uncertainty about its position accumulates and corrupts its knowledge of the positions from which observations are taken. We address this problem by employing cooperative localization; that is, deploying a second robot to observe the other as it explores, thereby establishing a virtual tether, and enabling an accurate estimate of the robot's position while it constructs the map. This paper presents our approach to this problem in the context of learning a set of visual landmarks useful for pose estimation. In addition to developing a formalism and concept, we validate our results experimentally and present quantitative results demonstrating the performance of the method.
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BibTeX
@inproceedings{Rekleitis2001a,
author = {Ioannis Rekleitis and Robert Sim and Gregory Dudek and
Evangelos Milios},
title = {Collaborative Exploration for Map Construction},
booktitle = {IEEE International Symposium on Computational
Intelligence in Robotics and Automation},
pages = {296-301},
year = {2001},
number = {ISBN 0-7803-7203-4},
address = {Banff, AB, Canada},
month = {Jul.},
organization = {IEEE}
}
Fri Apr 16 06:21:02 EDT 2021