SINS/Landmark Integrated Navigation Based on Landmark Attitude Determination

Based on the situation that the traditional SINS (strapdown inertial navigation system)/CNS (celestial navigation system) integrated navigation system fails to realize all-day and all-weather navigation, this paper proposes a SINS/Landmark integrated navigation method based on landmark attitude determination to solve this problem. This integrated navigation system takes SINS as the basic scheme and uses landmark navigation to correct the error of SINS. The way of the attitude determination is to use the landmark information photographed by the landmark camera to complete feature matching. The principle of the landmark navigation and the process of attitude determination are discussed, and the feasibility of landmark attitude determination is analyzed, including the orthogonality of the attitude transform matrix, as well as the influences of the factors such as quantity and geometric position of landmarks. On this basis, the paper constructs the equations of the SINS/Landmark integrated navigation system, testifies the effectiveness of landmark attitude determination on the integrated navigation by Kalman filter, and improves the navigation precision of the system.

[1]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[2]  Minjie Li,et al.  Image matching based on SIFT features and kd-tree , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[3]  Lie Wang,et al.  Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.

[4]  Emanuele Frontoni,et al.  A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks , 2010, J. Intell. Robotic Syst..

[5]  Justine Sherry,et al.  Helping conference attendees better understand research presentations , 2016, Commun. ACM.

[6]  Bhiksha Raj,et al.  Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Luitpold Babel.  Flight path planning for unmanned aerial vehicles with landmark-based visual navigation , 2014, Robotics Auton. Syst..

[8]  Ghassan Hamarneh,et al.  N-SIFT: N-DIMENSIONAL SCALE INVARIANT FEATURE TRANSFORM FOR MATCHING MEDICAL IMAGES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[9]  Ming Diao,et al.  Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network , 2015 .

[10]  Azriel Rosenfeld,et al.  Landmarks in Graphs , 1996, Discret. Appl. Math..

[11]  F. A. Smirnov,et al.  On space of integrable quantum field theories , 2016, 1608.05499.

[12]  Jian Han,et al.  Novel all-time initial alignment method for INS/CNS integrated navigation system , 2016, 2016 IEEE 13th International Conference on Signal Processing (ICSP).

[13]  Jiancheng Fang,et al.  A Fast Calibration Method of the Star Sensor Installation Error Based on Observability Analysis for the Tightly Coupled SINS/CNS-Integrated Navigation System , 2018, IEEE Sensors Journal.

[14]  Jitendra Malik,et al.  Unifying Map and Landmark Based Representations for Visual Navigation , 2017, ArXiv.

[15]  Jean-Philippe Thiran,et al.  Scale Invariant Feature Transform on the Sphere: Theory and Applications , 2011, International Journal of Computer Vision.

[16]  Anna Gorbenko,et al.  Computational Experiments for the Problem of Selection of a Minimal Set of Visual Landmarks , 2012 .

[17]  Lijun Song,et al.  Based on Grid Reference Frame for SINS/CNS Integrated Navigation System in the Polar Regions , 2019, Complex..

[18]  T. Voirin,et al.  Visual-inertial navigation for pinpoint planetary landing using scale-based landmark matching , 2016, Robotics Auton. Syst..

[19]  D. H. Sadler.  Astronomy and Navigation , 1950, Nature.

[20]  Li Suilao,et al.  Landmark based position and orientation method with tilt compensation for missile launcher , 2016, 2016 35th Chinese Control Conference (CCC).

[21]  Pini Gurfil,et al.  Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms , 2010, IEEE Transactions on Signal Processing.

[22]  Lingling Liu,et al.  A Two-Mode INS/CNS Navigation Method for Lunar Rovers , 2014, IEEE Transactions on Instrumentation and Measurement.

[23]  Dong-Hwan Hwang,et al.  Vision/INS Integrated Navigation System for Poor Vision Navigation Environments , 2016, Sensors.