Active Localization with Dynamic Obstacles

Alberto {Quattrini Li}, Marios Xanthidis, Jason O'Kane, Ioannis Rekleitis
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016.


Abstract: This paper addresses the problem of robot global localization in a known environment, in the presence of many dynamic obstacles. Deploying a robot in crowded spaces such as museums, shopping malls, department stores, or university campuses is especially challenging because the moving people occlude the static parts of the environment, such as walls and doorways, making the robot essentially blind. A new weighting function is proposed for a particle filter state estimation algorithm that accounts for the presence of dynamic obstacles and avoids population depletion. An active localization strategy is employed which guides the robot to locations that resolve ambiguities and eliminate hypotheses in a systematic manner. Experimental results from multiple simulations and from real robot deployments validate the localization improvements achieved by the proposed method.




  author       = {Alberto {Quattrini Li} and Marios Xanthidis and Jason
		 O'Kane and Ioannis Rekleitis},
  title        = {Active Localization with Dynamic Obstacles},
  booktitle    = {IEEE/RSJ International Conference on Intelligent Robots
		 and Systems (IROS)},
  year	       = {2016},
  month        = {Oct.},
  address      = {Daejeon, Korea}

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Wed Sep 18 06:21:01 EDT 2019