Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles
Accuracy and nonlinear processing capabilities are critical to the positioning and navigation of autonomous vehicles in visual–inertial odometry (VIO). Existing filtering-based VIO methods struggle to deal with strongly nonlinear systems and often exhibit low precision. To this end, this paper propo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/582 |
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| author | Guo Ma Cong Li Hui Jing Bing Kuang Ming Li Xiang Wang Guangyu Jia |
| author_facet | Guo Ma Cong Li Hui Jing Bing Kuang Ming Li Xiang Wang Guangyu Jia |
| author_sort | Guo Ma |
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| description | Accuracy and nonlinear processing capabilities are critical to the positioning and navigation of autonomous vehicles in visual–inertial odometry (VIO). Existing filtering-based VIO methods struggle to deal with strongly nonlinear systems and often exhibit low precision. To this end, this paper proposes a VIO method based on the Schur complement and Iterated Extended Kalman Filtering (IEKF). The algorithm first enhances ORB (Oriented FAST and Rotated BRIEF) features using Multi-Layer Perceptron (MLP) and Transformer architectures to improve feature robustness. It then integrates visual information and Inertial Measurement Unit (IMU) data through IEKF, constructing a complete residual model. The Schur complement is applied during covariance updates to compress the state dimension, improving computational efficiency and significantly enhancing the system’s ability to handle nonlinearities while maintaining real-time performance. Compared to traditional Extended Kalman Filtering (EKF), the proposed method demonstrates stronger stability and accuracy in high-dynamic scenarios. The experimental results show that the algorithm achieves superior state estimation performance on several typical visual–inertial datasets, demonstrating excellent accuracy and robustness. |
| format | Article |
| id | doaj-art-de07a9b19e6943cbbc68fa5e10514fb7 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-de07a9b19e6943cbbc68fa5e10514fb72025-08-20T03:58:31ZengMDPI AGMachines2075-17022025-07-0113758210.3390/machines13070582Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous VehiclesGuo Ma0Cong Li1Hui Jing2Bing Kuang3Ming Li4Xiang Wang5Guangyu Jia6Liuzhou Wuling Automobile Industry Co., Ltd., Liuzhou 545007, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Aerospace Technology, Guilin 541004, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541200, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541200, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541200, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541200, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541200, ChinaAccuracy and nonlinear processing capabilities are critical to the positioning and navigation of autonomous vehicles in visual–inertial odometry (VIO). Existing filtering-based VIO methods struggle to deal with strongly nonlinear systems and often exhibit low precision. To this end, this paper proposes a VIO method based on the Schur complement and Iterated Extended Kalman Filtering (IEKF). The algorithm first enhances ORB (Oriented FAST and Rotated BRIEF) features using Multi-Layer Perceptron (MLP) and Transformer architectures to improve feature robustness. It then integrates visual information and Inertial Measurement Unit (IMU) data through IEKF, constructing a complete residual model. The Schur complement is applied during covariance updates to compress the state dimension, improving computational efficiency and significantly enhancing the system’s ability to handle nonlinearities while maintaining real-time performance. Compared to traditional Extended Kalman Filtering (EKF), the proposed method demonstrates stronger stability and accuracy in high-dynamic scenarios. The experimental results show that the algorithm achieves superior state estimation performance on several typical visual–inertial datasets, demonstrating excellent accuracy and robustness.https://www.mdpi.com/2075-1702/13/7/582autonomous vehiclesVIOIEKFSchur complementMLPtransformer |
| spellingShingle | Guo Ma Cong Li Hui Jing Bing Kuang Ming Li Xiang Wang Guangyu Jia Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles Machines autonomous vehicles VIO IEKF Schur complement MLP transformer |
| title | Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles |
| title_full | Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles |
| title_fullStr | Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles |
| title_full_unstemmed | Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles |
| title_short | Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles |
| title_sort | schur complement optimized iterative ekf for visual inertial odometry in autonomous vehicles |
| topic | autonomous vehicles VIO IEKF Schur complement MLP transformer |
| url | https://www.mdpi.com/2075-1702/13/7/582 |
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