We propose a solution for sensor extrinsic self-calibration with low time complexity, competitive accuracy and graceful handling of often avoided corner cases: drift in calibration parameters and unobservable directions in the parameter space. It consists of three main parts: (1) information-theoretic based segment selection for constant-time estimation; (2) observability-aware parameter update through a rank-revealing decomposition of the Fischer information matrix; (3) drift-correcting self-calibration through the time-decay of segments. At the core of our FastCal algorithm is the loosely-coupled formulation for sensor extrinsics calibration and efficient selection of measurements. FastCal runs up to an order of magnitude faster than similar self-calibration algorithms (camera-to-camera extrinsics, excluding feature-matching and image pre-processing on all comparisons.), making FastCal ideal for integration into existing, resource-constrained, robotics systems.


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    Titel :

    FastCal: Robust Online Self-calibration for Robotic Systems


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Xiao, Jing (Herausgeber:in) / Kröger, Torsten (Herausgeber:in) / Khatib, Oussama (Herausgeber:in) / Nobre, Fernando (Autor:in) / Heckman, Christoffer R. (Autor:in)

    Kongress:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Erscheinungsdatum :

    2020-01-23


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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