A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 2
Apr.  2015

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Wei Shao, Shulin Sui, Lin Meng and Yaobin Yue, "Stable Estimation of Horizontal Velocity for Planetary Lander with Motion Constraints," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 2, pp. 198-206, 2015.
Citation: Wei Shao, Shulin Sui, Lin Meng and Yaobin Yue, "Stable Estimation of Horizontal Velocity for Planetary Lander with Motion Constraints," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 2, pp. 198-206, 2015.

Stable Estimation of Horizontal Velocity for Planetary Lander with Motion Constraints

Funds:

This work was supported by National Basic Research Program of China (973 Program) (2012CB720000), National Natural Science Foundation of China (61104187) and Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (BS2012NY003).

  • The planetary lander usually selects image feature points and tracks them from frame to frame in order to determine its own position and velocity during landing. Aiming to keep features tracking in consecutive frames, this paper proposes an approach of calculating the field of view (FOV) overlapping area in a 2D plane. Then the rotational and translational motion constraints of the lander can be found. If the FOVs intersects each other, the horizontal velocity of the lander is quickly estimated based on the least square method after the ill-conditioned matrices are eliminated previously. The Monte Carlo simulation results show that the proposed approach is not only able to recover the ego-motion of planetary lander, but also improves the stabilization performance. The relationship of the estimation error, running time and number of points is shown in the simulation results as well.

     

  • loading
  • [1]
    Johnson A, Willson R, Goguen J, Alexander J, Meller D. Field testing of the mars exploration rovers decent image motion estimation system. In:Proceedings of the 2005 International Conference Robotics and Automation. Barcelona, Spain:IEEE, 2005. 4463-4469
    [2]
    Tweddle B E. Computer Vision Based Navigation for Spacecraft Proximity Operations[Master dissertation], Massachusetts Institute of Technology, USA, 2010.
    [3]
    van Pham Bach V P, Lacroix Simon L, Devy Michel D. Vision-based absolute navigation for descent and landing. Journal of Field Robotics, 2012, 29(4):627-647
    [4]
    Cheng Y, Goguen J, Johnson A, Legetr C, Matthies L, Martin M S, Willson Ret al. The Mars exploration rovers descent image motion estimation system. In:Proceedings of the 2004 IEEE Intelligent Tutoring Systems, Los Alamitos, USA:IEEE, 2004. 13-21
    [5]
    Johnson A, Willson R, Cheng Y, Goguen J, Leger C, Sanmartin M, Matthies Let al. Design through operation of an image-based velocity estimation system for Mars landing. International Journal of Computer Vision, 2007, 74(3):319-341
    [6]
    Harris C, Stevens M. A combined corner and edge detector. In:Proceedings of the 4th Alvey Vision Conference. England:University of Manchester, 1988. 147-151
    [7]
    Flandin G, Polle B, Frapard B, Vidal P, Philippe C, Voirin T. Vision based navigation for planetary exploration. In:Proceedings of the 32nd Annual AAS Rocky Mountain Guidance and Control Conference. Breckenridge, Colorado:2009. 277-296
    [8]
    Lanza Piergiorgio L, Noceti Nicoletta N, Maddaleno Corrado M, Toma Antonio T, Zini Luca Z, Odone Francesca O. A vision-based navigation facility for planetary entry descent landing. In:Proceedings of the 12th International Conference on Computer Vision. Florence, Italy:Springer, 2012. 546-555
    [9]
    Rigatos Gerasimos G R. Nonlinear Kalman filters and particle filters for integrated navigation of unmanned aerial vehicles. Robotics and Autonomous Systems, 2012, 60(7):978-995
    [10]
    Li M Y, Mourikis Anastasion I M. High-precision, consistent EKF-based visual-inertial odometery. International Journal of Robotics Research, 2013, 32(6):690-711
    [11]
    Paul A J, Lorraine E P. A historical compilation of software metrics with applicability to NASA's Orion spacecraft flight software sizing. Innovations in Systems and Software Engineering, 2011, 7(3):161-170
    [12]
    Dubois M O, Parkes S, Dunstam M. Testing and validation of planetary vision-based navigation systems with PANGU. In:Proceedings of the 21st International Symposium on Space Flight Dynamics. Toulouse, France:ISSFD, 2009
    [13]
    Newcombe R A, Davison A J. Live dense reconstruction with a single moving camera. In:Proceedings of the 2010 International Conference on Computer Vision and Pattern Recognition. San Francisco, USA:IEEE, 2010. 1498-1505
    [14]
    Morel J M, Yu G S. ASIFT:a new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009, 2(2):438-469
    [15]
    Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(42):91-110
    [16]
    Bay H, Ess A, Tuytelaars T, van Gool L V. SURF:speeded up robust features. Computer Vision and Image Understanding, 2008, 110(3):346-359
    [17]
    Peris R, Marquina A, Candela V. The convergence of the perturbed Newton method and its application for ill-conditioned problems. Applied Mathematics and Computation, 2011, 218(7):2988-3001
    [18]
    Salahi M. On regularization of ill-conditioned linear systems. Journal of Applied Mathematics, 2008, 5(17):43-49
    [19]
    Brezinski C, Novati P, Redivo Z M. A rational Arnoldi approach for ill-conditioned linear systems. Journal of Computational and Applied Mathematics, 2012, 236(8):2063-2077

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 (1037) PDF downloads(11) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return