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.


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.



  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}

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