# Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
## 4 Conclusions  
This study analyzes the positioning accuracy of different sensor fusion methods in various environments, using Xtion and smartphone's satellite receiver, MEMS IMU for experiments. By using different methods to fuse the raw data collected from the smartphone and camera through adaptive filtering, three positioning results can be obtained: satellite positioning, GNSS/IMU integrated positioning, and GNSS/IMU/visual fusion positioning. An analysis and comparison of these three positioning methods reveal that in unobstructed environments, the STD of the GNSS/IMU/visual fusion positioning accuracy improves by 44.9% compared to satellite positioning and by 21.2% compared to GNSS/IMU integrated positioning. The RMSE of the GNSS/IMU/visual fusion positioning accuracy improves by 50.4% compared to satellite positioning and by 24.4% compared to GNSS/IMU integrated positioning. In obstructed environments, the STD of the GNSS/IMU/visual fusion positioning accuracy improves by 54.9% compared to satellite positioning and by 35.5% compared to GNSS/IMU integrated positioning. The RMSE of the GNSS/IMU/visual fusion positioning accuracy improves by 57.8% compared to satellite positioning and by 36.8% compared to GNSS/IMU integrated positioning. The experimental results show that the GNSS/IMU/visual fusion positioning can achieve satisfactory performance. However, when conducting experiments in open spaces such as a football field, some issues were observed. During the experiments, due to the high similarity of reference points between different frames, the camera would determine that the experimental subject had not moved, leading to the loss of some key frames. The next step in the research work is to update the algorithm to minimize the loss of key frames in special scenarios. [PERSON] is responsible for conducting the entire experiment and writing the entire paper. [PERSON], [PERSON] and [PERSON] are responsible for constructing the overall thought framework of the paper. [PERSON] provided guidance on the use of ORB-SLAM2. P.X. assisted in collecting experimental data. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant nos. 62263023; 41764002; 62161002). Not applicable. **Data Availability Statement:** All data used in this study was provided by Nanchang University. Due to privacy restrictions, the underlying raw data supporting the conclusions of this article will be made available by the authors on request. The authors extend their thanks to the National Natural Science Foundations of China (62263023, 41764002 and 62161002) and the Graduate Innovation Special Fund of Jiangxi Province of China (YC2024-S143) for their support for this paper. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{\begin{tabular}{c} **Experimental** \\ **Sequence** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Satellite** \\ **Positioning/m** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **GNSS/IMU/Visual** \\ **Fused** \\ **Positioning/m** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Accuracy** \\ **Improvement** \\ **Positioning** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy** \\ **Improvement** \\ **(Satellite \\ **Positioning)/\%** \\ \end{tabular} } \\ \hline \multirow{4}{*}{Unobstructed} & First & 4.1966 & 3.9723 & 2.7378 & 34.3 & 31.1 \\  & Second & 4.7599 & 2.4307 & 1.8422 & 61.3 & 24.2 \\ \cline{1-1}  & Third & 8.482 & 5.0603 & 4.0758 & 51.9 & 19.5 \\ \cline{1-1}  & Average & 5.8128 & 3.8178 & 2.8853 & 50.4 & 24.4 \\ \hline \multirow{4}{*}{Obstructed} & Fourth & 9.0871 & 6.7426 & 3.6645 & 59.7 & 45.7 \\ \cline{1-1}  & Fifth & 11.6945 & 5.7192 & 4.7475 & 59.4 & 17 \\ \cline{1-1}  & Sixth & 11.2584 & 8.9399 & 5.1084 & 54.6 & 42.9 \\ \cline{1-1}  & Average & 10.68 & 7.1339 & 4.5068 & 57.8 & 36.8 \\ \hline \hline \end{tabular}
\end{table}