# Fusion-Based Localization System Integrating UWB, IMU, and Vision
## 1 Introduction  
In modern society, the demand for high-precision positioning services has increased dramatically. Accurate and reliable localization plays a crucial role not only in national defense and emergency responses, but also in the rapid development of industrial automation, smart logistics, and autonomous navigation [1]. Although outdoor positioning technologies have become relatively mature, especially with the widespread use of global navigation satellite systems (GNSS), their applicability significantly declines in complex or obstructed environments [2]. In urban canyons, industrial sites, and indoor environments, GNSS signals are frequently blocked or reflected by surrounding structures. These disruptions lead to signal attenuation, multipath interference, and reduced satellite visibility, which in turn degrade positioning accuracy and reliability. As studies show that approximately 80-90% of humanactivities occur indoors, solving the challenge of accurate indoor positioning in GNSS-denied environments has become a key research direction [3]. Visual simultaneous localization and mapping (VSLAM) has emerged as a widely explored alternative [4]. VSLAM methods utilize monocular, stereo, or RGB-D cameras to estimate ego-motion and map unknown environments. However, in dynamic or texture-poor scenarios, visual methods alone often suffer from motion blur, occlusions, or lighting variations, leading to drift or even tracking failure. To address these issues, many recent studies incorporate inertial measurements, such as those from an inertial measurement unit (IMU), to complement visual data [5]. The combination results in visual-inertial odometry (VIO), which achieves high-frequency pose estimation and drift correction [6]. In parallel, wireless positioning technologies based on signal strength indicators--such as wireless local area network (WLAN), radio frequency identification (RFID), Bluetooth, and ZigBee--have seen rapid development in recent years [7]. These systems estimate position by analyzing the received signal strength (RSS) or signal patterns within known environments. However, due to their high sensitivity to environmental interference, such as multipath propagation and signal attenuation caused by walls or obstacles, their performance often suffers from instability and low accuracy. As a result, they are generally unsuitable for applications that demand reliable and robust localization, such as autonomous indoor robotics. On the other hand, ultra-wideband (UWB) has gained significant attention for indoor wireless positioning due to its ability to achieve centimeter-level accuracy, high time resolution, and strong resistance to multipath interference [8]. As an active ranging technology, UWB can provide global positioning constraints that help mitigate the drift of visual-inertial systems. Motivated by the complementary characteristics of VIO and UWB, this study aims to propose a loosely coupled multi-sensor fusion framework that combines UWB/IMU positioning with VIO to provide accurate and robust indoor localization. The system is designed to address the limitations of GNSS in indoor environments by leveraging the complementary strengths of global and relative positioning. Our approach offers a practical solution for reliable localization under challenging conditions. The main contributions of this paper are summarized as follows:  
1. A tightly coupled UWB/IMU localization model based on a sliding-window factor graph is proposed. This model constructs a cost function for state estimation by leveraging the localization principles of both UWB and IMU. 2. An indoor UWB and stereo VIO fusion localization method based on the EKF is proposed to enable long-term, accurate, and reliable real-time localization. The remainder of this paper is organized as follows:  
Section 3 presents the theoretical background of factor graphs and introduces a tightly coupled UWB/IMU fusion method based on factor graph optimization.