Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized for Visual Odometry (VO) and LiDAR Odometry (LO). Unfortunately, such measurements commonly suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion and the world is static. To this end, robust state estimation schemes are mandatory in order for humanoids to symbiotically co-exist with humans in their daily dynamic environments. In this article, the robust Gaussian Error-State Kalman Filter for humanoid robot locomotion is presented. The introduced method automatically detects and rejects outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Subsequently, the proposed method is quantitatively and qualitatively assessed in realistic conditions with the full-size humanoid robot WALK-MAN v2.0 and the mini-size humanoid robot NAO to demonstrate its accuracy and robustness when outlier VOLO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an open-source ROS/C++package.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Outlier-Robust State Estimation for Humanoid Robots*


    Beteiligte:
    Piperakis, S (Autor:in) / Kanoulas, D (Autor:in) / Tsagarakis, NG (Autor:in) / Trahanias, PE (Autor:in)

    Erscheinungsdatum :

    27.01.2020


    Anmerkungen:

    In: Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (pp. pp. 706-713). IEEE: Macau, China. (2020)


    Medientyp :

    Paper


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    DDC:    629




    Modelling, Estimation and Identification of Humanoid Robots Dynamics

    Traversaro, Silvio | BASE | 2017

    Freier Zugriff

    Statistical Learning for Humanoid Robots

    Vijayakumar, S. / D Souza, A. / Shibata, T. et al. | British Library Online Contents | 2002


    Technology enhanced learning using humanoid robots

    Diego Reforgiato | BASE | 2021

    Freier Zugriff