Solving the problem of autonomous drone navigation based on the integration of inertial and optical measurement systems

When building drone navigation systems, the main requirements for them are autonomy, accuracy and miniaturization of execution. Drone navigation autonomy can be achieved using strapdown, but its disadvantage is that the accuracy of solving the navigation problem deteriorates over time. To correct strapdown errors, its integration with various noninertial navigation systems is used, among which one of the most promising in terms of meeting the above requirements is the optical flow measurement navigation system. However, in its traditional use, only the components of the linear and angular velocities of unmanned aerial vehicles are determined. Such a determination of speeds is only part of the overall navigation task and does not allow us to solve it as a whole. In this regard, the article considers an approach that allows combining the capabilities of a free-form inertial navigation system that provides a solution to the navigation problem as a whole, and an optical flow navigation system that allows for autonomous monitoring of linear and angular motion parameters with minimal hardware costs. The proposed solution to the drones autonomous navigation problem is based on the strongly coupled integration of strapdown and an optical flow navigation system using stochastic nonlinear filtering methods. The synthesis of the navigation algorithm is based on the formation of equations of the estimated vector of navigation parameters based on inertial measurements, and the equations of its observer based on optical flow measurements, followed by the implementation of a single navigation filter based on them, taking into account the discrete nature of the measurements used. To estimate the full vector of motion parameters of drones based on measurements of the integrated inertial optical navigation system, a modified extended discrete Kalman filter was used for correlated object and observer noise. The proposed approach was tested on the basis of a numerical experiment during which the spatial and angular motion of a medium-speed drone was modeled with the simultaneous formation of noisy measurements of its motion parameters. The measurement interference level is selected according to the interference level of the medium-range inertial and optical meters. The algorithm for estimating the vector of navigation parameters of the drone is implemented based on the proposed modified extended discrete filter Kalman. The obtained error values for estimating all drone motion parameters have shown that it is possible to meet the accuracy requirements of not only modern, but also promising autonomous navigation systems. The highly coupled integration of inertial and optical navigation systems in terms of computational costs and accuracy of estimating motion parameters turns out to be more effective than the traditional method of determining only the components of the linear and angular velocities of an object based on the parameters of the optical flow. The main advantages of the proposed inertial optical navigation system are autonomy and the ability to monitor all motion parameters of an unmanned aerial vehicle. The stability and accuracy of the assessment, the simplicity of the technical implementation make it possible to use the proposed solution for autonomous noise-resistant navigation of drones for various purposes.

1. Veremeenko K.K., Zheltov S.Iu., Kim N.V. Modern Information Technologies in the Tasks of Navigation and Guidance of Unmanned Aerial Vehicles. Moscow, Fizmatlit Publ., 2009, 552 p. (in Russian)

2. Perov A.I., Kharisov V.N. (ed.) GLONASS: Design Concepts and Principles of Operation. Moscow, Radiotehnika Publ., 2010, 800 p. (in Russian)

3. Shaheen E.M. Mathematical analysis for the GPS carrier tracking loop phase jitter in presence of different types of interference signals. Gyroscopy and Navigation, 2018, vol. 9, no. 4, pp. 267–276. https://doi.org/10.1134/s2075108718040077

4. Bhatti J., Humphreys T.E. Hostile control of ships via false GPS signals: demonstration and detection. Navigation, 2017, vol. 64, no. 1, pp. 51–66. https://doi.org/10.1002/navi.183

5. Siniutin S.A., Sokolov S.V. Solution of the problem of close integration of inertial-satellite navigation systems, complexed with odometer. Ingineering Journal of Don, 2014, no. 4-1 (31), pp 74. (in Russian)

6. Emeliantcev G.I., Stepanov A.P Integrated Inertial-Satellite Systems of Orientation and Navigation. St. Petersburg, Concern CSRI Elektropribor, 2016, 394 p. (in Russian)

7. Anuchin N.O., Emeliantcev G.I. Integrated Systems of Orientation and Navigation for Marine Moving Objects. St. Petersburg, Concern CSRI Elektropribor, 1999, 356 p. (in Russian)

8. Rozenberg I.N., Sokolov S.V., Umanskii V.I., Pogorelov V.A. Theoretical Framework of the Deep Integration of Inertial-Satellite Navigation Systems. Moscow, Fizmatlit Publ., 2018, 305 p. (in Russian)

9. Stepovoi A.V. Methods for estimating the probability of an aircraft guidance with a passive optoelectronic device. Journal of Instrument Engineering, 1999, vol. 42, no. 2, pp. 40–44. (in Russian)

10. Kitt B., Geiger A., Lategahn H. Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. Proc. of the IEEE Intelligent Vehicles Symposium, 2010, pp. 486–492. https://doi.org/10.1109/ivs.2010.5548123

11. Bruss A.R., Horn B.K.P. Passive navigation. Computer Vision, Graphics, and Image Processing, 1983, vol. 21, no. 1, pp. 3–20. https://doi.org/10.1016/s0734-189x(83)80026-7

12. Geiger A., Lenz P., Stiller C., Urtasun R. Vision meets robotics: The KITTI Dataset. The International Journal of Robotics Research, 2013, v o l . 3 2 , n o . 11 , p p . 1 2 3 1 – 1 2 3 7 . https://doi.org/10.1177/0278364913491297

13. Raudies F., Neumann H. A review and evaluation of methods estimating ego-motion. Computer Vision and Image Understanding, 2012, vol. 116, no. 5, pp. 606–633. https://doi.org/10.1016/j.cviu.2011.04.004

14. Zhang T., Tomasi C. On the consistency of instantaneous rigid motion estimation. International Journal of Computer Vision, 2002, vol. 46, no. 1, pp. 51–79. https://doi.org/10.1023/a:1013248231976

15. Horn B.K.P. Robot Vision. Mit Pr, 1986, 480 p.

16. Ponomarev E.S., Grigorev A.S. Algorithms for optical flow calculations in the problem of the proper motion determining. Proc. of the 39th Information Technologies and Systems, 2015, pp. 457–470. (in Russian)

17. Baker S., Roth S., Scharstein D., Black M.J., Lewis J.P., Szeliski R. A database and evaluation methodology for optical flow. Proc. of the IEEE 11th International Conference on Computer Vision, 2007, pp. 1–8. https://doi.org/10.1109/iccv.2007.4408903

18. Fleet D.J., Weiss Y. Optical flow estimation. Handbook of Mathematical Models in Computer Vision, 2006, pp. 237–257. https:// doi.org/10.1007/0-387-28831-7_15

19. Sokolov S.V., Shvidchenko S.A., Reshetnikova I.V., Vavilova E.V. Effective estimation of motion parameters of mobile robotic complexes based on information processing of technical vision systems. Proc. of the Systems of signals generating and processing in the field of on board communications, 2025, pp. 1–4. https://doi.org/10.1109/ieeeconf64229.2025.10948069

20. Kanatani K. 3-D Interpretation of optical flow by renormalization. International Journal of Computer Vision, 1993, vol. 11, no. 3, pp. 267–282. https://doi.org/10.1007/BF01469345

21. Mirabdollah H., Mertsching B. On the Second Order Statistics of Essential Matrix Elements. Lecture Notes in Computer Science, 2014, vol. 8753, pp. 547–557. https://doi.org/10.1007/978-3-319-11752-2_45

22. Xu L., Jia J., Matsushita Y. Motion detail preserving optical flow estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, vol. 34, no.9, pp. 1744–1757. https://doi.org/10.1109/TPAMI.2011.236

23. Zach C., Pock T., Bischof H. A duality based approach for realtime TV-L 1 optical flow. Lecture Notes in Computer Science, 2007, vol. 4713, pp. 214–223. https://doi.org/10.1007/978-3-540-74936-3_22

24. Tikhonov V.I., Kharisov V.N. Statistical Analysis and Synthesis of Radio Engineering Devices and Systems. Moscow, Radio i svjaz’ Publ., 2004, 608 p. (in Russian)

25. Ishlinskii A.Iu. Orientation, Gyroscopes, and Inertial Navigation. Moscow, Nauka Publ., 1976. 670 p. (in Russian)