Single scene and path reconstruction with a monocular camera using integral concurrent learning

An observer is developed that uses a monocular camera to reconstruct the positions of stationary features in a scene and the path the camera travels through the scene in real time. A Lyapunov based stability analysis is provided to show the observer is globally exponentially stable for less restrictive conditions than current literature. A simulation is provided to validate the developed theory and demonstrate performance.

[1]  Darren M. Dawson,et al.  Euclidean position estimation of static features using a moving camera with known velocities , 2007, 2007 46th IEEE Conference on Decision and Control.

[2]  Girish Chowdhary,et al.  Concurrent learning adaptive control of linear systems with exponentially convergent bounds , 2013 .

[3]  Anup Parikh,et al.  Adaptive control of a surface marine craft with parameter identification using integral concurrent learning , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[4]  Stefano Soatto,et al.  Structure from Motion Causally Integrated Over Time , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Guoqiang Hu,et al.  Lyapunov-Based Range Identification For Paracatadioptric Systems , 2006, IEEE Transactions on Automatic Control.

[6]  D. Dawson,et al.  Range identification for perspective vision systems , 2003, Proceedings of the 2003 American Control Conference, 2003..

[7]  Alessandro Astolfi,et al.  A new solution to the problem of range identification in perspective vision systems , 2005, IEEE Transactions on Automatic Control.

[8]  Florian Holzapfel,et al.  Concurrent Learning Adaptive Model Predictive Control , 2013 .

[9]  D. Mayne Nonlinear and Adaptive Control Design [Book Review] , 1996, IEEE Transactions on Automatic Control.

[10]  Giuseppe Oriolo,et al.  Feature Depth Observation for Image-based Visual Servoing: Theory and Experiments , 2008, Int. J. Robotics Res..

[11]  W. Dixon,et al.  Lyapunov-based range and motion identification for a nonaffine perspective dynamic system , 2006, 2006 American Control Conference.

[12]  G. Chowdhary,et al.  A singular value maximizing data recording algorithm for concurrent learning , 2011, Proceedings of the 2011 American Control Conference.

[13]  Richard I. Hartley,et al.  Iterative Extensions of the Sturm/Triggs Algorithm: Convergence and Nonconvergence , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Guoqiang Hu,et al.  Lyapunov-Based Range Identification For A Paracatadioptric System , 2006, CDC.

[15]  Emanuel Aldea,et al.  SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion , 2011, 2012 American Control Conference (ACC).

[16]  Xinkai Chen,et al.  State observer for a class of nonlinear systems and its application to machine vision , 2004, IEEE Transactions on Automatic Control.

[17]  M. Boutayeb,et al.  Convergence analysis of the extended Kalman filter used as an observer for nonlinear deterministic discrete-time systems , 1997, IEEE Trans. Autom. Control..

[18]  Domenico Prattichizzo,et al.  Range estimation from a moving camera: An Immersion and Invariance approach , 2009, 2009 IEEE International Conference on Robotics and Automation.

[19]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[20]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[21]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[22]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Warren E. Dixon,et al.  Nonlinear Control of Engineering Systems , 2002 .

[24]  Anup Parikh,et al.  Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives , 2015, ArXiv.

[25]  Warren E. Dixon,et al.  Globally exponentially stable observer for vision-based range estimation , 2012 .

[26]  Konrad Reif,et al.  The extended Kalman filter as an exponential observer for nonlinear systems , 1999, IEEE Trans. Signal Process..

[27]  John Oliensis,et al.  A Critique of Structure-from-Motion Algorithms , 2000, Comput. Vis. Image Underst..

[28]  Bijoy K. Ghosh,et al.  Single camera based motion and shape estimation using extended Kalman filtering , 2001 .

[29]  Larry H. Matthies,et al.  Kalman filter-based algorithms for estimating depth from image sequences , 1989, International Journal of Computer Vision.

[30]  Rushikesh Kamalapurkar,et al.  Concurrent learning-based approximate optimal regulation , 2013, 52nd IEEE Conference on Decision and Control.

[31]  P. Perona,et al.  Motion estimation via dynamic vision , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[32]  Richard I. Hartley,et al.  Multiple-View Geometry Under the {$L_\infty$}-Norm , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Eric N. Johnson,et al.  Theory and Flight-Test Validation of a Concurrent-Learning Adaptive Controller , 2011 .