Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas
In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitati...
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MDPI AG
2025-08-01
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| author | Yangzi Cong Wenbin Su Nan Jiang Wenpeng Zong Long Li Yan Xu Tianhe Xu Paipai Wu |
| author_facet | Yangzi Cong Wenbin Su Nan Jiang Wenpeng Zong Long Li Yan Xu Tianhe Xu Paipai Wu |
| author_sort | Yangzi Cong |
| collection | DOAJ |
| description | In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors. |
| format | Article |
| id | doaj-art-b67b926ea8494efa8383a4d8bdfeac25 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-b67b926ea8494efa8383a4d8bdfeac252025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-08-012515474510.3390/s25154745Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian FormulasYangzi Cong0Wenbin Su1Nan Jiang2Wenpeng Zong3Long Li4Yan Xu5Tianhe Xu6Paipai Wu7Institute of Space Sciences, Shandong University, Weihai 264209, ChinaChina Research Institute of Radio Wave Propagation, Qingdao 266107, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaXi’an Research Institute of Surveying and Mapping, Xi’an 710054, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaIn a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors.https://www.mdpi.com/1424-8220/25/15/4745drones/UAVmotion blurimage quality assessmentvisual–inertial navigation systemadaptive covariance matrix |
| spellingShingle | Yangzi Cong Wenbin Su Nan Jiang Wenpeng Zong Long Li Yan Xu Tianhe Xu Paipai Wu Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas Sensors drones/UAV motion blur image quality assessment visual–inertial navigation system adaptive covariance matrix |
| title | Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas |
| title_full | Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas |
| title_fullStr | Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas |
| title_full_unstemmed | Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas |
| title_short | Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas |
| title_sort | adaptive covariance matrix for uav based visual inertial navigation systems using gaussian formulas |
| topic | drones/UAV motion blur image quality assessment visual–inertial navigation system adaptive covariance matrix |
| url | https://www.mdpi.com/1424-8220/25/15/4745 |
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