Active Localization with Dynamic Obstacles

Alberto Quattrini Li, Marios Xanthidis, Jason M. O'Kane, and Ioannis Rekleitis
In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems
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.



@inproceedings{QuaXan+16,
  author = {Alberto Quattrini Li and Marios Xanthidis and Jason M. O'Kane and and
            Ioannis Rekleitis},
  booktitle = {Proc. IEEE/RSJ International Conference on Intelligent Robots and
               Systems},
  title = {Active Localization with Dynamic Obstacles},
  year = {2016}
}


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