The probabilistic belief networks that result from standard feature-based simultaneous localization and map building (SLAM) approaches cannot be directly used to plan trajectories. The reason is that they produce a sparse graph of landmark estimates and their probabilistic relations, which is of little value to find collision free paths for navigation. In contrast, we argue in this paper that Pose SLAM graphs can be directly used as belief roadmaps (BRMs). The original BRM algorithm assumes a known model of the environment from which probabilistic sampling generates a roadmap. In our work, the roadmap is built on-line by the Pose SLAM algorithm. The result is a hybrid BRM-Pose SLAM method that devises optimal navigation strategies on-line by searching for the path with lowest accumulated uncertainty for the robot pose. The method is validated over synthetic data and standard SLAM datasets. ; Postprint (published version)


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Path Planning in Belief Space with Pose SLAM

    Valencia, Rafael / Andrade-Cetto, Juan | Springer Verlag | 2017


    Path planning in belief space with pose SLAM

    Valencia, Rafael / Andrade-Cetto, Juan / Porta, Josep M. | BASE | 2011

    Freier Zugriff

    Mapping, planning and exploration with Pose SLAM

    Valencia, Rafael / Andrade-Cetto, Juan | TIBKAT | 2017


    Mapping, planning and exploration with Pose SLAM

    Valencia Carreño, Rafael | BASE | 2013

    Freier Zugriff

    Mapping, planning and exploration with Pose SLAM

    Valencia Carreño, Rafael | BASE | 2013

    Freier Zugriff