A square root unscented Kalman filter for multiple view geometry based stereo cameras/inertial navigation

Exact motion estimation is one of the major tasks in autonomous navigation. Conventional Global Positioning System-aided inertial navigation systems are able to provide accurate locations. However, they are limited when used in a Global Positioning System-denied environment. In this paper, we present a square root unscented Kalman filter-based approach for navigation by using stereo cameras and an inertial sensor only. The main contribution of this work is the development of a novel measurement model by applying multiple view geometry constraints to the stereo cameras/inertial system. The measurement model does not require the three-dimensional feature position in the state vector of the filter, which substantially reduces the size of the state vector and the computational burden. To incorporate this nonlinear and complex measurement model, a variant of the square root unscented Kalman filter-based algorithm is also proposed. The root of the state covariance is propagated and updated directly in the square root unscented Kalman filter, thereby avoiding the decomposition of the state covariance and improving the stability of our algorithm. Experimental results based on a real outdoor dataset are presented to demonstrate the feasibility and the accuracy of the proposed approach.

[1]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[2]  Xiaoping Hu,et al.  Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach , 2015 .

[3]  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.

[4]  Dah-Jye Lee,et al.  Matching Affine Features with the SYBA Feature Descriptor , 2014, ISVC.

[5]  Seth J. Teller,et al.  Epipolar Constraints for Vision-Aided Inertial Navigation , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[6]  Michael Veth,et al.  Fusing Low-Cost Image and Inertial Sensors for Passive Navigation , 2007 .

[7]  Jwu-Sheng Hu,et al.  A sliding-window visual-IMU odometer based on tri-focal tensor geometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Gaurav S. Sukhatme,et al.  Combined Visual and Inertial Navigation for an Unmanned Aerial Vehicle , 2008, FSR.

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

[10]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

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

[12]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[13]  P. Handel,et al.  Realtime implementation of visual-aided inertial navigation using epipolar constraints , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[14]  Salah Sukkarieh,et al.  Airborne simultaneous localisation and map building , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[15]  Gaurav S. Sukhatme,et al.  Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration , 2011, Int. J. Robotics Res..

[16]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[17]  Jorge Dias,et al.  Relative Pose Calibration Between Visual and Inertial Sensors , 2007, Int. J. Robotics Res..

[18]  Mikel M. Miller,et al.  Navigation in GPS Denied Environments: Feature-Aided Inertial Systems , 2010 .

[19]  Dah-Jye Lee,et al.  Visual Odometry Drift Reduction Using SYBA Descriptor and Feature Transformation , 2016, IEEE Transactions on Intelligent Transportation Systems.

[20]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[21]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Stergios I. Roumeliotis,et al.  A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation , 2008, IEEE Transactions on Robotics.

[23]  Peter Corke,et al.  An Introduction to Inertial and Visual Sensing , 2007, Int. J. Robotics Res..

[24]  J. Kuipers.  Quaternions and Rotation Sequences , 1998 .

[25]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[26]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[27]  David W. Murray,et al.  Mobile Robot Localisation Using Active Vision , 1998, ECCV.

[28]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[29]  David W. Murray,et al.  Simultaneous Localization and Map-Building Using Active Vision , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[31]  F. Fraundorfer,et al.  Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications , 2012, IEEE Robotics & Automation Magazine.

[32]  Rogelio Lozano,et al.  Combining Stereo Vision and Inertial Navigation System for a Quad-Rotor UAV , 2011, J. Intell. Robotic Syst..

[33]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[35]  A. Pinz,et al.  Calibration of Hybrid Vision / Inertial Tracking Systems * , 2005 .

[36]  Dah-Jye Lee,et al.  An efficient feature descriptor based on synthetic basis functions and uniqueness matching strategy , 2016, Comput. Vis. Image Underst..

[37]  Wenqi Wu,et al.  Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors , 2012, Sensors.

[38]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[39]  Ehud Rivlin,et al.  Real-Time Vision-Aided Localization and Navigation Based on Three-View Geometry , 2012, IEEE Transactions on Aerospace and Electronic Systems.