Fusion-Based Localization System Integrating UWB, IMU, and Vision
Accurate indoor positioning services have become increasingly important in modern applications. Various new indoor positioning methods have been developed. Among them, visual–inertial odometry (VIO)-based techniques are notably limited by lighting conditions, while ultrawideband (UWB)-based algorith...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6501 |
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| author | Zhongliang Deng Haiming Luo Xiangchuan Gao Peijia Liu |
| author_facet | Zhongliang Deng Haiming Luo Xiangchuan Gao Peijia Liu |
| author_sort | Zhongliang Deng |
| collection | DOAJ |
| description | Accurate indoor positioning services have become increasingly important in modern applications. Various new indoor positioning methods have been developed. Among them, visual–inertial odometry (VIO)-based techniques are notably limited by lighting conditions, while ultrawideband (UWB)-based algorithms are highly susceptible to environmental interference. To address these limitations, this study proposes a hybrid indoor positioning algorithm that combines UWB and VIO. The method first utilizes a tightly coupled UWB/inertial measurement unit (IMU) fusion algorithm based on a sliding-window factor graph to obtain initial position estimates. These estimates are then combined with VIO outputs to formulate the system’s motion and observation models. Finally, an extended Kalman filter (EKF) is applied for data fusion to achieve optimal state estimation. The proposed hybrid positioning algorithm is validated on a self-developed mobile platform in an indoor environment. Experimental results show that, in indoor environments, the proposed method reduces the root mean square error (RMSE) by 67.6% and the maximum error by approximately 67.9% compared with the standalone UWB method. Compared with the stereo VIO model, the RMSE and maximum error are reduced by 55.4% and 60.4%, respectively. Furthermore, compared with the UWB/IMU fusion model, the proposed method achieves a 50.0% reduction in RMSE and a 59.1% reduction in maximum error. |
| format | Article |
| id | doaj-art-7777fbabf01e4fc298a22edddea75752 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7777fbabf01e4fc298a22edddea757522025-08-20T03:32:28ZengMDPI AGApplied Sciences2076-34172025-06-011512650110.3390/app15126501Fusion-Based Localization System Integrating UWB, IMU, and VisionZhongliang Deng0Haiming Luo1Xiangchuan Gao2Peijia Liu3School of Electronics and Information, Zhengzhou University of Aeronautics, Zhengzhou 450046, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaAccurate indoor positioning services have become increasingly important in modern applications. Various new indoor positioning methods have been developed. Among them, visual–inertial odometry (VIO)-based techniques are notably limited by lighting conditions, while ultrawideband (UWB)-based algorithms are highly susceptible to environmental interference. To address these limitations, this study proposes a hybrid indoor positioning algorithm that combines UWB and VIO. The method first utilizes a tightly coupled UWB/inertial measurement unit (IMU) fusion algorithm based on a sliding-window factor graph to obtain initial position estimates. These estimates are then combined with VIO outputs to formulate the system’s motion and observation models. Finally, an extended Kalman filter (EKF) is applied for data fusion to achieve optimal state estimation. The proposed hybrid positioning algorithm is validated on a self-developed mobile platform in an indoor environment. Experimental results show that, in indoor environments, the proposed method reduces the root mean square error (RMSE) by 67.6% and the maximum error by approximately 67.9% compared with the standalone UWB method. Compared with the stereo VIO model, the RMSE and maximum error are reduced by 55.4% and 60.4%, respectively. Furthermore, compared with the UWB/IMU fusion model, the proposed method achieves a 50.0% reduction in RMSE and a 59.1% reduction in maximum error.https://www.mdpi.com/2076-3417/15/12/6501indoor positioningultrawideband (UWB)visual–inertial SLAMsensor fusionKalman filter |
| spellingShingle | Zhongliang Deng Haiming Luo Xiangchuan Gao Peijia Liu Fusion-Based Localization System Integrating UWB, IMU, and Vision Applied Sciences indoor positioning ultrawideband (UWB) visual–inertial SLAM sensor fusion Kalman filter |
| title | Fusion-Based Localization System Integrating UWB, IMU, and Vision |
| title_full | Fusion-Based Localization System Integrating UWB, IMU, and Vision |
| title_fullStr | Fusion-Based Localization System Integrating UWB, IMU, and Vision |
| title_full_unstemmed | Fusion-Based Localization System Integrating UWB, IMU, and Vision |
| title_short | Fusion-Based Localization System Integrating UWB, IMU, and Vision |
| title_sort | fusion based localization system integrating uwb imu and vision |
| topic | indoor positioning ultrawideband (UWB) visual–inertial SLAM sensor fusion Kalman filter |
| url | https://www.mdpi.com/2076-3417/15/12/6501 |
| work_keys_str_mv | AT zhongliangdeng fusionbasedlocalizationsystemintegratinguwbimuandvision AT haimingluo fusionbasedlocalizationsystemintegratinguwbimuandvision AT xiangchuangao fusionbasedlocalizationsystemintegratinguwbimuandvision AT peijialiu fusionbasedlocalizationsystemintegratinguwbimuandvision |