Minimum Sigma Set SR-UKF for Quadrifocal Tensor-based Binocular Stereo Vision-IMU Tightly-coupled System

This paper presents a binocular vision-IMU (Inertial Measurement Unit) tightly-coupled structure based on a Minimum sigma set Square-Root Unscented Kalman Filter (M-SRUKF) for real time navigation applications. Though the M-SRUKF has only half the sigma points of the SRUKF, it has the same accuracy as the SRUKF when applied to the binocular vision-IMU tightly-coupled system. As the Kalman filter flow is a kind of square-root system, the stability of the system can be guaranteed. The measurement model and the outlier rejection model of this tightly-coupled system not only utilises the epipolar constraint and the trifocal tensor geometry constraint between the consecutive two image pairs, but also uses the quadrifocal tensor geometry among four views. The structure of the binocular vision-IMU tightly-coupled system is in the form of an error state, and the time updates of the state and the state covariance are directly estimated without using Unscented Transformation (UT). Experiments are carried out based on an outdoor land vehicle open source dataset and an indoor Micro Aerial Vehicle (MAV) open source dataset. Results clearly show the effectiveness of the proposed new mechanisation.

[1]  Yonggang Zhang,et al.  Robust student’s t based nonlinear filter and smoother , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Lilian Zhang,et al.  Line primitives and their applications in geometric computer vision , 2013 .

[3]  Carlo L. Bottasso,et al.  Tightly-coupled stereo vision-aided inertial navigation using feature-based motion sensors , 2014, Adv. Robotics.

[4]  Henrique Marra Menegaz,et al.  A new smallest sigma set for the Unscented Transform and its applications on SLAM , 2011, IEEE Conference on Decision and Control and European Control Conference.

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

[6]  Qiang Fang,et al.  UKF for Integrated Vision and Inertial Sensors Based on Three-View Geometry , 2013, IEEE Sensors Journal.

[7]  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.

[8]  N. El-Sheimy,et al.  Comparisons of SR-UKF Family for a Visual-IMU Tightly-coupled System Based on Tri-focal Tensor Geometry , 2017 .

[9]  Dimitrios G. Kottas,et al.  Efficient and consistent vision-aided inertial navigation using line observations , 2013, 2013 IEEE International Conference on Robotics and Automation.

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

[11]  Alonzo Kelly,et al.  A new approach to vision-aided inertial navigation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[13]  Xiaofeng He,et al.  A square root unscented Kalman filter for multiple view geometry based stereo cameras/inertial navigation , 2016 .

[14]  Wei Ping,et al.  A novel simplex unscented transform and filter , 2007, 2007 International Symposium on Communications and Information Technologies.

[15]  Henrique Marra Menegaz,et al.  A Systematization of the Unscented Kalman Filter Theory , 2015, IEEE Transactions on Automatic Control.

[16]  Richard I. Hartley,et al.  Computation of the Quadrifocal Tensor , 1998, ECCV.

[17]  Wenqi Wu,et al.  Tightly-Coupled Stereo Visual-Inertial Navigation Using Point and Line Features , 2015, Sensors.

[18]  Yang Gao,et al.  Implementation and Analysis of Tightly Integrated INS/Stereo VO for Land Vehicle Navigation , 2017, Journal of Navigation.

[19]  Roland Siegwart,et al.  The EuRoC micro aerial vehicle datasets , 2016, Int. J. Robotics Res..

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

[21]  A. Aydin Alatan,et al.  Loosely coupled Kalman filtering for fusion of Visual Odometry and inertial navigation , 2013, Proceedings of the 16th International Conference on Information Fusion.

[22]  Dimitrios G. Kottas,et al.  Camera-IMU-based localization: Observability analysis and consistency improvement , 2014, Int. J. Robotics Res..

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

[24]  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).

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

[26]  Carlo L. Bottasso,et al.  Tightly-coupled vision-aided inertial navigation via trifocal constraints , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[27]  Simon J. Julier,et al.  The spherical simplex unscented transformation , 2003, Proceedings of the 2003 American Control Conference, 2003..

[28]  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).

[29]  Jeffrey K. Uhlmann,et al.  Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[30]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[32]  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.

[33]  Olivier D. Faugeras,et al.  On the geometry and algebra of the point and line correspondences between N images , 1995, Proceedings of IEEE International Conference on Computer Vision.

[34]  Yonggang Zhang,et al.  A Robust Gaussian Approximate Fixed-Interval Smoother for Nonlinear Systems With Heavy-Tailed Process and Measurement Noises , 2016, IEEE Signal Processing Letters.

[35]  Andreas Geiger,et al.  Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme , 2010, 2010 IEEE Intelligent Vehicles Symposium.