Citation
Nikolay A. Atanasov, Menglong Zhu, Kostas Daniilidis, George Pappas. "Semantic Localization Via the Matrix Permanent". Robotics: Science and Systems (RSS), July, 2014.

Abstract
Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot's sensors. The task of the robot is to localize itself within a prior landmark-based map, in which the landmarks are annotated with semantic classes. As object recognition algorithms miss detections and produce false alarms, correct data association is central to the semantic localization problem. Instead of the traditional vector-based representations, we use random finite sets (RFS) to represent the map and the object detections. Bayesian filtering in the resulting RFS space offers a natural solution to the data association and clutter filtering problems. Our main contribution is to reduce the problem of computing the likelihood of an RFS, required in the update equation of the set-based Bayes filter, to the problem of computing a matrix permanent. It is this crucial transformation that enables us to use polynomial-time algorithms to carry out semantic Monte-Carlo localization. The performance of our approach is demonstrated in simulation and in a real environment using a deformable-part-model object detector. Comparisons are made with the traditional lidar-based geometric Monte-Carlo localization.

Electronic downloads

• https://www.src.org/library/publication/p069971/. (Link to paper on SRC website.)
• Atanasov_etal_SemanticLocalization_RSS14.mp4 · video/mp4 · 5579 kbytes. (Video demonstrating the semantic localization)
• http://www.roboticsproceedings.org/rss10/p43.html
• http://www.seas.upenn.edu/~atanasov/ref/Atanasov_SemanticLocalization_RSS14.pdf

• HTML

  Nikolay A. Atanasov, Menglong Zhu, Kostas Daniilidis, George
  Pappas. <a
  href="http://www.terraswarm.org/pubs/305.html"
  >Semantic Localization Via the Matrix
  Permanent</a>, Robotics: Science and Systems (RSS),
  July, 2014.

• Plain text

  Nikolay A. Atanasov, Menglong Zhu, Kostas Daniilidis, George
  Pappas. "Semantic Localization Via the Matrix
  Permanent". Robotics: Science and Systems (RSS), July,
  2014.

• BibTeX

  @inproceedings{AtanasovZhuDaniilidisPappas14_SemanticLocalizationViaMatrixPermanent,
      author = {Nikolay A. Atanasov and Menglong Zhu and Kostas
                Daniilidis and George Pappas},
      title = {Semantic Localization Via the Matrix Permanent},
      booktitle = {Robotics: Science and Systems (RSS)},
      month = {July},
      year = {2014},
      abstract = {Most approaches to robot localization rely on
                low-level geometric features such as points,
                lines, and planes. In this paper, we use object
                recognition to obtain semantic information from
                the robot's sensors. The task of the robot is to
                localize itself within a prior landmark-based map,
                in which the landmarks are annotated with semantic
                classes. As object recognition algorithms miss
                detections and produce false alarms, correct data
                association is central to the semantic
                localization problem. Instead of the traditional
                vector-based representations, we use random finite
                sets (RFS) to represent the map and the object
                detections. Bayesian filtering in the resulting
                RFS space offers a natural solution to the data
                association and clutter filtering problems. Our
                main contribution is to reduce the problem of
                computing the likelihood of an RFS, required in
                the update equation of the set-based Bayes filter,
                to the problem of computing a matrix permanent. It
                is this crucial transformation that enables us to
                use polynomial-time algorithms to carry out
                semantic Monte-Carlo localization. The performance
                of our approach is demonstrated in simulation and
                in a real environment using a
                deformable-part-model object detector. Comparisons
                are made with the traditional lidar-based
                geometric Monte-Carlo localization.},
      URL = {http://terraswarm.org/pubs/305.html}
  }
  

Posted by Nikolay A. Atanasov on 22 Apr 2014.
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