## 4 Conclusions and Future Research Direction  
In this study, a study of a position estimation system for developing driving assistance technology for mapless spaces such as wheelchair getting on/off spaces that are difficult for autonomous driving technology to navigate was conducted. In order to implement a high-accuracy position estimation system, this study was conducted to estimate a high-accuracy position and direction by fusing UWB/IMU/encoder sensors. As a result of fusing the data of each sensor, the low-accuracy problem of the general UWB sensor-based position estimation was corrected through the data from the IMU and encoder sensors built into the electric wheelchair, and based on this, the position and direction of 15 cm and 15.3\({}^{\circ}\) or less were estimated. Since the position and direction can be updated by receiving each sensor's data in real time, it is expected that the driving assistance system can be implemented using the position estimation method proposed in this study. In order to implement a position estimation system based on a UWB sensor, which is characterized by noise and outliers, pre-processing is required to reduce the impact of noise and outliers in  
Figure 13: Position estimation experimental graphs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Raw UWB** & **Encoder** & **Fusion** \\ \hline \((0,0)\) & \((0,0)\) & \((0,0)\) & \((0,0)\) \\ \hline \((0,150)\) & \((11,154)\) & \((14,166)\) & \((-8,155)\) \\ \hline \((0,300)\) & \((1,291)\) & \((51,325)\) & \((-12,296)\) \\ \hline \((100,300)\) & \((112,280)\) & \((136,298)\) & \((103,295)\) \\ \hline \((200,300)\) & \((199,271)\) & \((253,261)\) & \((207,296)\) \\ \hline \((200,150)\) & \((193,156)\) & \((189,110)\) & \((205,147)\) \\ \hline \((200,0)\) & \((189,22)\) & \((144,-24)\) & \((200,12)\) \\ \hline \((100,0)\) & \((103,18)\) & \((41,10)\) & \((99,7)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Position estimation experiment results. the sensor data. In this paper, encoder and IMU sensor-based data pre-processing is used to estimate the odometry of a wheelchair to improve the accuracy of a single UWB sensor. In addition, it is expected that the accuracy of the UWB sensor-based position estimation system proposed in this paper can be further improved through the fusion of LiDAR and camera sensors. In the future, research will be conducted to ensure the right to move by assisting the operation ability of those who have difficulties in operating wheelchairs through the process of getting on/off wheelchair vehicles and implementing a driving assistance system in the entrance. Conceptualization and methodology, E.J. ; software, E.J. and S.-H.E. ; validation, E.J. and S.-H.E. ; formal analysis, E.J. and S.-H.E. ; investigation, E.J. and E.-H.L. and S.-H.E. ; data curation, E.J. ; visualization, E.J. ; writing--original draft preparation, E.J., S.-H.E. and E.-H.L. ; writing--review and editing, E.J. and E.-H.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2024-2020-0-01741) supervised by the IITP (Institute for Information & communications Technology Planning and Evaluation). This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety) (Project Number: RS-2020-KD000162). This research was supported by Culture, Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture, Sports and Tourism in 2024 (RS-2023-00218166). Not applicable. The datasets generated during this study are available from the corresponding author on reasonable request. The data are not publicly available due to privacy.