# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 2 Background of Analysis
### Navigation Service
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technology Type** & **Specific Technology** & **Benefits \& Estimates** & **Limitations \& Challenges** & **Refs.** \\ \hline \multirow{6}{*}{Wireless Communication} & Wi-Fi + & - Uses existing APs & - Low distance accuracy & \multirow{2}{*}{[10]} \\  & Lateration & - Low setup cost & - Sensitive to signal reflections & \\ \cline{2-5}  & Wi-Fi + & - Higher accuracy in mapped areas & - Time-consuming offline mapping & \multirow{2}{*}{[10]} \\  & RSS fingerprinting & - No special setup on user devices & - Performance varies with environment & \\  & & - Hard to scale in large spaces & \\ \cline{2-5}  & Wireless & - Enhanced Localization Accuracy & - Intrinsic Limitations of BLE Signals & \multirow{2}{*}{[12; 13; 14]} \\  & BLE beacon + Neural networks & - Adaptability to Environmental Variations & - Data and Training Requirements & \\  & & - Performance Enhancement & - Device and Environment Dependency & \\ \cline{2-5}  & BLE beacon + Filtering & - Accuracy improvement and error reduction & - Implementation and computational complexity & \\  & & - Noise and variability handling & - Intrinsic limitations of BLE signals & \\ \cline{2-5}  & & - High accuracy & - Infrastructure costs & \multirow{2}{*}{[10; 15]} \\  & UWD & - Robust signal penetration & - Energy consumption & \\  & & - Extended range & - & \\ \hline \multirow{6}{*}{Sensor} & GPS + & - Outdoor precision & - Limited positional accuracy & \multirow{2}{*}{[16; 17]} \\  & Transformer or ML & - Enhanced portability and usability & - Inaccurate distance estimation & \\ \cline{2-5}  & ML & - Improved accessibility & - Data fusion & \\ \cline{2-5}  & RGB-D camera + ML & - Provision of enriched environmental information & - Data noise and distortion & \multirow{2}{*}{[15; 18]} \\  & ML & - Real-time processing & \\ \cline{2-5}  & IMU & - Posture estimation & - Cumulative drift error & [11; 16; 18] \\ \hline \multirow{2}{*}{Marker-based} & Two-dimensional code & - High positioning accuracy & - Low distance estimation accuracy & \multirow{2}{*}{[19; 20; 21]} \\  & code & - Fast information retrieval & - Requires environmental installation & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Navigation technologies. UWB technology is widely used for indoor localization, particularly known for its high accuracy, capable of estimating locations within a few centimeters, with certain cases demonstrating positional errors under 20 cm. Compared to other RF technologies, UWB offers a wider transmission range and may require fewer devices than RFID-based systems. However, effective operation of UWB systems necessitates obstacle avoidance capabilities. Although UWB is currently considered the most accurate indoor positioning system available, extensive infrastructure deployment, involving numerous UWB anchors, is required. Furthermore, compatible end-user devices have yet to achieve widespread adoption [10; 15]. Several navigation methods integrating deep reinforcement learning and neural networks with GPS-based technologies have been explored. Smart assistive systems integrate ML algorithms like YOLO for real-time object recognition combined with ultrasonic sensors and GPS navigation, to enhance user support [17]. ASSISTER uses imitation learning to generate natural-language navigation guidance based on visual input and GPS/IMU localization [16]. These methods offer advantages including improved positional accuracy through multi-sensor fusion, detailed environmental information from deep learning models, real-time object detection, customized guidance, portability, integrated functionality, and enhanced accessibility. However, challenges remain, such as limited positional accuracy in complex urban settings, limitations in object detection accuracy, challenges in distance estimation, dataset acquisition and labeling difficulties, system complexity, real-time performance issues, privacy concerns, environmental constraints, user interaction complexities, and power efficiency considerations.