Fusion-Based Localization System Integrating UWB, IMU, and Vision
Zhongliang Deng, Haiming Luo, Xiangchuan Gao, Peijia Liu
Abstract
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.