Scan Matching in a probabilistic framework

Abstract – This paper describes an interpretation of scan matching as a probability distribution approximation problem and proposes an algorithm that, employing a particle approximation to the target distribution, can take advantage of the knowledge of the evolution model and provide an estimate of the matching uncertainty. Experiments show it can work in unstructured environments, it is reliable to severe sensor occlusions and it handles under constrained situations gracefully.

• A. Censi, "Scan Matching in a probabilistic framework" ( PDF, final version).
  

  @inproceedings{censi06gpm,
       author = {Censi, Andrea},
        title = {Scan matching in a probabilistic framework},
    booktitle = {Proceedings of the IEEE International Conference 
                 on Robotics and Automation (ICRA)},
        pages = {2291--2296},
         year = {2006},
      address = {Orlando, Florida},
          url = { http://purl.org/censi/2006/gpm}
  }

• slides used at ICRA (PDF, 2MB)