Welcome to Journal of Beijing Institute of Technology
Volume 26Issue 2
.
Turn off MathJax
Article Contents
Zheng Rong, Shun'an Zhong, Nathan Michael. Online Detection of State Estimator Performance Degradation via Efficient Numerical Observability Analysis[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2017, 26(2): 259-266. doi: 10.15918/j.jbit1004-0579.201726.0216
Citation: Zheng Rong, Shun'an Zhong, Nathan Michael. Online Detection of State Estimator Performance Degradation via Efficient Numerical Observability Analysis[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2017, 26(2): 259-266.doi:10.15918/j.jbit1004-0579.201726.0216

Online Detection of State Estimator Performance Degradation via Efficient Numerical Observability Analysis

doi:10.15918/j.jbit1004-0579.201726.0216
  • Received Date:2016-12-15
  • An efficient observability analysis method is proposed to enable online detection of performance degradation of an optimization-based sliding window visual-inertial state estimation framework. The proposed methodology leverages numerical techniques in nonlinear observability analysis to enable online evaluation of the system observability and indication of the state estimation performance. Specifically, an empirical observability Gramian based approach is introduced to efficiently measure the observability condition of the windowed nonlinear system, and a scalar index is proposed to quantify the average system observability. The proposed approach is specialized to a challenging optimization-based sliding window monocular visual-inertial state estimation formulation and evaluated through simulation and experiments to assess the efficacy of the methodology. The analysis result shows that the proposed approach can correctly indicate degradation of the state estimation accuracy with real-time performance.
  • loading
  • [1]
    Lupton T, Sukkarieh S. Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions[J]. IEEE Transactions on Robotics,2012, 28(1):61-76.
    [2]
    Hinson B T. Observability-based guidance and sensor placement[D]. Washington:University of Washington, 2014.
    [3]
    Alaeddini A, Morgansen K A. Trajectory design for a nonlinear system to insure observability[C]//2014 European Control Conference (ECC), Strasbourg, France, 2014.
    [4]
    Huang G P, Mourikis A I, Roumeliotis S I. An observability-constrained sliding window filter for SLAM[C]//2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, CA, USA, 2011.
    [5]
    Rong Z, Michael N. Detection and prediction of near-term state estimation degradation via online nonlinear observability analysis[C]//2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Lausanne, Switzerland, 2016.
    [6]
    Hinson B T, Binder M K, Morgansen K A. Path planning to optimize observability in a planar uniform flow field[C]//2013 American Control Conference (ACC), Washington, DC, USA, 2013.
    [7]
    Qi J, Sun K, Kang W. Optimal PMU placement for power system dynamic state estimation by using empirical observability gramian[J]. IEEE Transactions on Power Systems,2015, 30(4):2041-2054.
    [8]
    Singh A K, Hahn J. On the use of empirical gramians for controllability and observability analysis[C]//American Control Conference, Portland, Oregon, USA, 2005.
    [9]
    Lall S, Marsden J E, Glavaški S. Empirical model reduction of controlled nonlinear systems[C]//IFAC World Congress, New York, 1999.
    [10]
    Himpe C, Ohlberger M. A unified software framework for empirical gramians[J]. Journal of Mathematics,2013, 2013(1):6.
    [11]
    Krener A J, Ide K. Measures of unobservability[C]//48th IEEE Conference on Decision and Control (CDC/CCC), Shanghai, China, 2009.
    [12]
    Shen S, Mulgaonkar Y, Michael N, et al. Initialization-free monocular visual-inertial estimation with application to autonomous MAVs[C]//International Symposium on Experimental Robotics (ISER), Essaouira, Morocco, 2014.
    [13]
    Shen S J, Michael N, Kumar V. Tightly-coupled monocular visual-inertial fusion for autonomous flight of rotorcraft MAVs[C]//2015 IEEE International Conference on Robotics and Automation (ICRA), Washington, USA, 2015.
    [14]
    Loianno G, Mulgaonkar Y, Brunner C, et al. Smartphones power flying robots[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015.
    [15]
    Hansen P, Alismail H, Rander P, et al. Monocular visual odometry for robot localization in LNG pipes[C]//2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 2011.
    [16]
    Sibley G, Matthies L, Sukhatme G. Sliding window filter with application to planetary landing[J]. Journal of Field Robotics,2010, 27(5):587-608.
    [17]
    Zhang J, Kaess M, Singh S. On degeneracy of optimization-based state estimation problems[C]//2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016.
    [18]
    Qi J, Sun K, Kang W. Adaptive optimal PMU placement based on empirical observability gramian[C]//10th IFAC Symposium on Nonlinear Control Systems NOLCOS, Monterey, California, USA, 2016.
    [19]
    Forster C, Carlone L, Dellaert F, et al. On-manifold preintegration theory for fast and accurate visual-inertial navigation[EB/OL].[2017-02-12]. https://pdfs.semanticscholar.org/ed4e/9f89d1fbcf50bea8c65b947b6397a61b4945.pdf.
    [20]
    Agarwal S, Mierle K. Ceres solver[EB/OL].[2016-08-14]. http://ceres-solver.org/.
  • 加载中

Catalog

    通讯作者:陈斌, bchen63@163.com
    • 1.

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (923) PDF downloads(415) Cited by()
    Proportional views
    Related

    /

      Return
      Return
        Baidu
        map