In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where each object can move independently from the background and still be effectively tracked and its shape fused over time using only the information from pixels associated with that object label. Previous attempts to deal with dynamic scenes have typically considered moving regions as outliers, and consequently do not model their shape or track their motion over time. In contrast, we enable the robot to maintain 3D models for each of the segmented objects and to improve them over time through fusion. As a result, our system can enable a robot to maintain a scene description at the object level which has the potential to allow interactions with its working environment; even in the case of dynamic scenes.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Co-fusion: Real-time segmentation, tracking and fusion of multiple objects


    Beteiligte:
    Runz, M (Autor:in) / Agapito, L (Autor:in)

    Erscheinungsdatum :

    24.07.2017


    Anmerkungen:

    In: (Proceedings) 2017 IEEE International Conference on Robotics and Automation (ICRA). (pp. pp. 4471-4478). IEEE: Singapore. (2017)


    Medientyp :

    Paper


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    DDC:    629



    PWP3D: Real-Time Segmentation and Tracking of 3D Objects

    Prisacariu, V. A. / Reid, I. D. | British Library Online Contents | 2012


    Segmentation-based tracking by support fusion

    Heber, M. / Godec, M. / Ruther, M. et al. | British Library Online Contents | 2013


    Continuous Global Evidence-Based Bayesian Modality Fusion for Simultaneous Tracking of Multiple Objects

    Sherrah, J. / Gong, S. / IEEE | British Library Conference Proceedings | 2001


    Fusion of perceptual processes for real-time object tracking

    Jüngling, K. / Arens, M. / Hanheide, M. et al. | Fraunhofer Publica | 2008

    Freier Zugriff