Collaborative exploration for the construction of visual maps

Ioannis Rekleitis, Robert Sim, Gregory Dudek, Evangelos Milios
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2001. [33 citations]

Abstract

Abstract: We examine the problem of learning a visual map of the environment while maintaining an accurate pose estimate. Our approach is based on using two robots in a simple collaborative scheme; in practice, one of these robots can be much less capable than the other. In many mapping contexts, a robot moves about collecting data (images, in particular) which are later used to assemble a map; we can think of map construction as a training process. Without outside information, as a robot collects training images, its position estimate accumulates errors, thus corrupting its knowledge of the positions from which observations are taken. We address this problem by deploying a second robot to observe the first one as it explores, thereby establishing a \emph{virtual tether}, and enabling an accurate estimate of the robot's position while it constructs the map. We refer to this process as \emph{cooperative localization}. The images collected during this process are assembled into a representation that allows vision-based position estimation from a single image at a later date. In addition to developing a formalism and concept, we validate our results experimentally and present quantitative results demonstrating the performance of the method in over 90 trials.

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BibTeX

@inproceedings{Rekleitis2001b,
  author       = {Ioannis Rekleitis and Robert Sim and Gregory Dudek and
		 Evangelos Milios},
  title        = {Collaborative exploration for the construction of visual
		 maps},
  booktitle    = {IEEE/RSJ International Conference on Intelligent Robots
		 and Systems (IROS)},
  pages        = {1269-1274},
  year	       = {2001},
  volume       = {3},
  number       = {ISBN 0-7803-6614-X},
  address      = {Maui, HI, USA},
  month        = {Oct.},
  organization = {IEEE/RSJ}
}

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Mon Sep 16 06:21:02 EDT 2019