## 6 Conclusions  
In this work, we proposed a novel autonomous personnel localization method to address the need for rescuers to achieve autonomous localization in complex environments. The proposed method integrates and enhances two technologies: PDR and VIO solutions. Additionally, we conducted feature selection for model training based on data characteristic analysis and established an adaptive step length estimation model to tackle the challenges of low accuracy and poor robustness in estimating rescuers' step lengths in complex environments. Furthermore, step length data were incorporated into the visual localization algorithm to effectively improve the robustness of the visual localization system in complex environments. Subsequently, by employing a Kalman filter, we successfully fused VIO data with APDR data to achieve high-precision localization. Finally, a low-cost, wearable autonomous positioning vest system was designed, which demonstrated excellent performance during testing. Our proposed solution facilitates autonomous localization of rescue personnel in complex post-disaster environments. Experimental results demonstrate that the proposed localization system significantly reduces localization errors and outperforms standalone PDR and VIO solutions in terms of overall error, extreme error, and error fluctuations. Moreover, compared to state-of-the-art VIO technologies, our system achieves superior localization accuracy. Despite its effectiveness across multiple scenarios, the system still has certain limitations. Firstly, although our adaptive step length estimation and fusion algorithms have \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Path**} & \multirow{2}{*}{**Method**} & **Minimum Error (m)** & **Maximum Error (m)** & **Average Error (m)** & **Standard deviation (m)** & **Endpoint Error (m)** \\ \hline \multirow{4}{*}{Path A} & APDR & 0.08508 & 6.1771 & 2.30863 & 1.79395 & 6.17711 \\  & VIO & 0.02879 & 1.46039 & 0.60778 & 0.39585 & 1.24573 \\  & OURS & 0.07829 & 0.74569 & 0.39962 & 0.20471 & 0.66439 \\  & APDR & 0.05205 & 4.55375 & 1.54539 & 0.78236 & 4.55375 \\ \multirow{4}{*}{Path B} & VIO & 0.04364 & 2.21872 & 0.77853 & 0.53609 & 2.21872 \\  & OURS & 0.09929 & 0.68558 & 0.40072 & 0.13246 & 0.64760 \\  & APDR & 0.07018 & 16.00896 & 7.61376 & 4.47056 & 11.27938 \\ \multirow{4}{*}{Path C} & VIO & 0.05963 & 3.04747 & 1.82674 & 0.85140 & 2.36798 \\  & OURS & 0.13079 & 2.40783 & 0.86502 & 0.39598 & 2.40783 \\ \hline \end{tabular}
\end{table} 
Table 3: Trajectory error statistics. significantly reduced errors, challenges such as time synchronization issues and higher computational demands may arise in complex environments, potentially impacting real-time performance. In future work, we aim to extend our method to more challenging environments, enhancing its adaptability and robustness in real-world complex scenarios. Furthermore, our ongoing efforts will focus on the integrated design of low-power and miniaturized sensors, developing an integrated MEMS sensor array that packages accelerometers, gyroscopes, magnetometers, and miniature binocular cameras on a flexible substrate to achieve lightweight and low-power goals for the vast system. Additional improvements may include integrating more sensor types, optimizing sensor fusion algorithms to boost localization accuracy and stability. Conceptualization, T.T., Y.H. and X.Z. ; investigation, T.T., X.Z., H.Z. and Z.L. ; methodology, T.T., Y.H. and H.Z. ; software, T.T. and Z.L. ; formal analysis, Y.H. and Y.W. ; data curation, T.T. and Z.L. roles/writing--original draft, T.T. ; writing--review and editing, Y.H. and Y.W. ; supervision Y.W. ; funding acquisition, Y.H. ; validation, T.T. ; visualization, T.T. and X.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (Grant No. 52478123), and the National Key Research and Development Program of China, grant number 2020 YC1511704.