Suppression of Drift in Vehicle Inertial Navigation without Satellite Signals Using Zero-velocity Updates during Complete Stops and Visual Reference
DOI:
https://doi.org/10.24160/1993-6982-2026-3-191-197Keywords:
inertial navigation, ZUPT, zero-velocity update, visual odometry, visual georegistration, visual SLAM, extended Kalman filter, ROS 2, wheel odometry, non-holonomic constraints, GNSS-denied navigation, multisensor fusion, automotive navigationAbstract
Loss or degradation of satellite navigation signals in urban canyons, tunnels, and indoor environments leads to unstable vehicle positioning and rapid growth of inertial navigation error. The article proposes a computerized autonomous navigation system without the use of satellite signals that combines wheel–inertial dead reckoning with two complementary correction sources: zero-velocity updates during complete standstills and frontal camera-based visual reference. The system uses accelerometers and gyroscopes, vehicle bus data, and images. The state estimation takes kinematic (non-holonomic) constraints into account. The driving through a tunnel and real-world runs along an urban route including a multilevel parking lot are simulated. Four navigation modes (inertial only, inertial with zero-velocity updates, inertial with visual correction, and combined one ) were compared. The comparison results have shown that zero-velocity updates bound error growth between stops in a steplike manner, while visual reference eliminates long-term drift. The combined mode maintains mean position error at fractions of a percent of the distance traveled and keeps the absolute error below 10 m after extended intervals without satellite signals. The solution is implementable on readily available hardware and is suitable for robotic and driver-assistance systems.
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Для цитирования: Маркова Д.А. Подавление дрейфа инерциальной навигации автомобиля без спутниковых сигналов методами обновлений по нулевой скорости при полной остановке и визуальной привязке // Вестник МЭИ. 2026. № 3. С. 191—197. DOI: 10.24160/1993-6982-2026-3-191-197.
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1. Moussa M. e. a. Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment. Sensors. 2020;20(22):6567.
2. Liu F. e. a. Implementation and Analysis of Tightly Integrated INS/Stereo VO for Land Vehicle Navigation. J. Navigation. 2017;71(1):1—17.
3. Kilic C. e. a. ZUPT-aided GNSS Factor Graph with Inertial Navigation Integration for Wheeled Robots. Proc. 34th Intern. Tech. Meeting of the Satellite Division of the Institute of Navigation. St. Louis, 2021:3285—3293.
4. Wang H. e. a. Heading Reference-assisted Pose Estimation for Ground Vehicles. IEEE Trans. Automation Sci. Eng. 2018;16(1):448—458.
5. Mur-Artal R., Tardós J.D. ORB-SLAM2: an Open-source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Trans. Robotics. 2017;33(5):1255—1262.
6. Gakne P.V., O'Keefe K. Tackling the Scale Factor Issue in a Monocular Visual Odometry Using a 3D City Model. Proc. Intern. Tech. Symp. Navigation and Timing. Toulouse, 2018:1—11.
7. Rublee E., Rabaud V., Konolige K., Bradski G. ORB: an Efficient Alternative to SIFT or SURF. Proc. Intern. Conf. Computer Vision. Barcelona, 2011:2564—2571.
8. Rosten E., Drummond T. Machine Learning for High-speed Corner Detection. Lecture Notes in Computer Sci. Pt. 1. 2006;3951:430—443.
9. Lowe D.G. Distinctive Image Features from Scale-invariant Keypoints. Intern. J. Computer Vision. 2004;60(2):91—110.
10. Bay H., Tuytelaars T., Van Gool L. SURF: Speeded up Robust Features. Lecture Notes in Computer Sci. Pt. 1. 2006;3951(3):404—417.
11. Lepetit V., Moreno-Noguer F., Fua P. EPnP: an Accurate O(n) Solution to the PnP Problem. Intern. J. Computer Vision. 2009;81(2):155—166.
12. Geneva P. e. a. OpenVINS: a Research Platform for Visual-inertial Estimation. Proc. IEEE Intern. Conf. Robotics and Automation. Paris, 2020:4666—4672.
13. Rosinol A., Abate M., Chang Y., Carlone L. Kimera: an Open-source Library for Real-time Metric-semantic Localization and Mapping. Ibid., Pp. 1689—1696.
14. Usenko V. e. a. Visual-inertial Mapping with Non-linear Factor Recovery. IEEE Robotics and Automation Lett. 2020;5(2):422—429
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For citation: Markova D.A. Suppression of Drift in Vehicle Inertial Navigation without Satellite Signals Using Zero-velocity Updates during Complete Stops and Visual Reference. Bulletin of MPEI. 2026;3:191—197. (in Russian). DOI: 10.24160/1993-6982-2026-3-191-197
