Efficient and consistent vision-aided inertial navigation using line observations

This paper addresses the problem of estimating the state of a vehicle moving in 3D based on inertial measurements and visual observations of lines. In particular, we investigate the observability properties of the corresponding vision-aided inertial navigation system (VINS) and prove that it has five (four) unobservable degrees of freedom when one (two or more) line(s) is (are) detected. Additionally, we leverage this result to improve the consistency of the extended Kalman filter (EKF) estimator introduced for efficiently processing line observations over a sliding time-window at cost only linear in the number of line features. Finally, we validate the proposed algorithm experimentally using a miniature-size camera and a micro-electromechanical systems (MEMS)-quality inertial measurement unit (IMU).

[1]  Dimitrios G. Kottas,et al.  Observability-constrained Vision-aided Inertial Navigation , 2012 .

[2]  Dimitrios G. Kottas,et al.  Towards Consistent Vision-Aided Inertial Navigation , 2012, WAFR.

[3]  Cordelia Schmid,et al.  Automatic line matching across views , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Adrien Bartoli,et al.  Structure-from-motion using lines: Representation, triangulation, and bundle adjustment , 2005, Comput. Vis. Image Underst..

[5]  Stefano Soatto,et al.  Edge descriptors for robust wide-baseline correspondence , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Roland Siegwart,et al.  Real-time metric state estimation for modular vision-inertial systems , 2011, 2011 IEEE International Conference on Robotics and Automation.

[7]  David J. Kriegman,et al.  Structure and Motion from Line Segments in Multiple Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Agostino Martinelli,et al.  Vision and IMU Data Fusion: Closed-Form Solutions for Attitude, Speed, Absolute Scale, and Bias Determination , 2012, IEEE Transactions on Robotics.

[9]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[10]  Minas E. Spetsakis,et al.  Structure from motion using line correspondences , 1990, International Journal of Computer Vision.

[11]  Ian D. Reid,et al.  Real-Time Monocular SLAM with Straight Lines , 2006, BMVC.

[12]  Bijoy K. Ghosh,et al.  Pose estimation using line-based dynamic vision and inertial sensors , 2003, IEEE Trans. Autom. Control..

[13]  Stergios I. Roumeliotis,et al.  A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[14]  Roland Siegwart,et al.  Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[15]  Yuanxin Wu,et al.  On 'A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation' , 2013, ArXiv.

[16]  Stergios I. Roumeliotis,et al.  Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing , 2009, IEEE Transactions on Robotics.

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

[18]  N. Trawny,et al.  Indirect Kalman Filter for 3 D Attitude Estimation , 2005 .

[19]  Dimitrios G. Kottas,et al.  On the Consistency of Vision-Aided Inertial Navigation , 2012, ISER.

[20]  Salah Sukkarieh,et al.  Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions , 2012, IEEE Transactions on Robotics.

[21]  Narendra Ahuja,et al.  Motion and Structure from Line Correspondences; Closed-Form Solution, Uniqueness, and Optimization , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Stergios I. Roumeliotis,et al.  Optimal estimation of vanishing points in a Manhattan world , 2011, 2011 International Conference on Computer Vision.

[23]  Homer H. Chen.  Pose Determination from Line-to-Plane Correspondences: Existence Condition and Closed-Form Solutions , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Stefano Soatto,et al.  Visual-inertial navigation, mapping and localization: A scalable real-time causal approach , 2011, Int. J. Robotics Res..

[25]  Peter S. Maybeck,et al.  Stochastic Models, Estimation And Control , 2012 .

[26]  Jorge Dias,et al.  Vision and Inertial Sensor Cooperation Using Gravity as a Vertical Reference , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Mingyang Li,et al.  Improving the accuracy of EKF-based visual-inertial odometry , 2012, 2012 IEEE International Conference on Robotics and Automation.

[28]  Jiri Matas,et al.  Robust Detection of Lines Using the Progressive Probabilistic Hough Transform , 2000, Comput. Vis. Image Underst..

[29]  Olivier D. Faugeras,et al.  Maintaining representations of the environment of a mobile robot , 1988, IEEE Trans. Robotics Autom..

[30]  Stergios I. Roumeliotis,et al.  Stochastic cloning: a generalized framework for processing relative state measurements , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[31]  Larry H. Matthies,et al.  Feature and pose constrained visual Aided Inertial Navigation for computationally constrained aerial vehicles , 2011, 2011 IEEE International Conference on Robotics and Automation.

[32]  Thomas S. Huang,et al.  Estimation of rigid body motion using straight line correspondences , 1986, Comput. Vis. Graph. Image Process..

[33]  Roland Siegwart,et al.  Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV , 2012, 2012 IEEE International Conference on Robotics and Automation.

[34]  Stergios I. Roumeliotis,et al.  Globally optimal pose estimation from line correspondences , 2011, 2011 IEEE International Conference on Robotics and Automation.