Relying on static representations of the environment limits the use of mapping methods in most real-world tasks. Real-world environments are dynamic and undergo changes that need to be handled through map adaptation. In this work, an object-based pose graph is proposed to solve the problem of mapping in indoor dynamic environments with mobile robots. In contrast to state-of-the art methods where binary classifications between movable and static objects are used, we propose a new method to capture the probability of different objects over time. Object probability represents how likely it is to find a specific object in its previous location and it gives a quantification of how movable specific objects are. In addition, grouping object probabilities according to object class allows us to evaluate the movability of different object classes. We validate our object-based pose graph in real-world dynamic environments. Results in mapping and map adaptation with a real robot show efficient map maintenance through several mapping sessions and results in object classification according to movability show an improvement compared to binary classification. ; This work was supported in part by the HEROITEA: Heterogeneous Intelligent Multi-Robot Team for Assistance of Elderly People under Grant RTI2018-095599-B-C21 and in part by Spanish Ministerio de Economia y Competitividad and the RoboCity2030- DIH-CM Project under Grant S2018/NMT-4331, RoboCity2030 - Madrid Robotics Digital Innovation Hub. This work was supported in part by the European Regional Development Fund under the project Robotics for Industry 4.0 under Grant CZ.02.1.01/0.0/0.0/15_003/0000470 and in part by the Grant Agency of the Czech Technical University in Prague, under Grant SGS19/174/OHK3/3T/13.


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    Title :

    Object-based pose graph for dynamic indoor environments



    Publication date :

    2020-10-01


    Remarks:

    AR/0000025954



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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