# Fusion-Based Localization System Integrating UWB, IMU, and Vision
## 3 Tightly Coupled UWB/IMU Fusion Localization Based on Sliding-Window Factor Graph
### UIWB/IMU Fusion Localization
It is evident that the system maintains high localization accuracy in the early phase of motion, but the trajectory gradually drifts over time due to cumulative error. The maximum localization error reaches 0.4997 m, especially when returning to the initial position. The UWB/IMU fusion model was used for localization estimation and compared with the ground-truth trajectory, as shown in Figure 8a. Compared to the UWB-only system, the fusion model demonstrates higher localization accuracy, with reduced drift in regions far from the anchor nodes. The overall trajectory aligns more closely with the actual walking path. Although the factor graph-based UWB/IMU fusion model incorporates more historical states and observations, some drift still occurs in the latter part of the trajectory, resulting in increased error. The maximum localization error in this case is 0.3947 m.

Figure 8: (**a**) The UWB/IMU fusion model localization results and ground-truth trajectory; (**b**) the proposed model in this study’s localization results and ground-truth trajectory. The proposed model in this study was also used for localization estimation and was compared against the actual walking trajectory, as shown in Figure 8b. The EKF-based stereo VIO and UWB fusion system achieved superior localization accuracy, effectively addressing the drift commonly observed in VIO-only systems, while also mitigating the impact of UWB NLOS errors on the fusion system. As a result, the estimated trajectory is more consistent with the reference path. The maximum localization error of the fusion system is 0.2465 m.

Figure 9 presents the statistical localization errors for UWB, stereo VIO, UWB/IMU fusion models, and stereo VIO/UWB fusion models. As shown in Table 1, compared with the UWB-only method, the proposed algorithm improves localization accuracy by 67.6% and reduces the maximum error by approximately 67.9%. Compared with the stereo VIO model, the accuracy improves by 55.4%, and the maximum error decreases by about 60.4%. Compared with the UWB/IMU fusion model, it improves accuracy by 50.0% and reduces the maximum error by approximately 59.1%. ## 6 Conclusions

To address the limitations of stereo VIO--such as drift and significant localization errors in indoor environments--and the susceptibility of UWB to large errors under NLOS conditions, this study recognizes that single-sensor systems still struggle to meet the stability requirements of high-precision navigation. To overcome these challenges, this paper adopts the EKF to fuse UWB/IMU localization data with stereo VIO observations, and develops a UWB-VIO hybrid localization system based on the ROS platform for mobile

\begin{table}
\begin{tabular}{c c c} \hline \hline
**Localization Algorithm** & **Error Metrics** & **Error Magnitude (m)** \\ \hline UWB & RMSE & 0.4212 \\  & MAX & 0.9297 \\ \hline VIO & RMSE & 0.3052 \\  & MAX & 0.7778 \\ \hline UWB/IMU & RMSE & 0.2722 \\  & MAX & 0.7515 \\ \hline Proposed Algorithm & RMSE & 0.1361 \\  & MAX & 0.30



robot applications. Comparative experiments demonstrate that the proposed method significantly improves localization accuracy and robustness in complex indoor environments. It effectively mitigates the drift problem in VIO and enhances the system's resistance to NLOS interference. Although the proposed filter-based fusion method provides good real-time performance and meets the continuous localization needs of indoor mobile robots, it still lags behind state-of-the-art graph-based optimization methods in terms of localization accuracy. Therefore, future work may explore the integration of filtering and optimization strategies to balance real-time performance with accuracy. In addition, the current method does not explicitly address the influence of environmental interference, including RF noise, signal multipath, or visual occlusions. These factors may degrade localization performance in dynamic or cluttered environments.