# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 2 Background of Analysis
### Navigation Service
Visual Simultaneous Localization and Mapping (VSLAM) systems utilize RGB-D cameras to estimate device position and orientation without prior mapping, integrating deep learning techniques to enhance path planning capabilities. RGB-D sensors combine visual and depth information, supporting real-time egomotion estimation, mapping, and obstacle-aware path planning [15]. This approach offers several advantages, including improved positional accuracy through multi-sensor fusion, enriched environmental data provided by RGB-D sensors, real-time object detection capabilities, and enhanced portability, usability, and accessibility [15; 18]. Nevertheless, several challenges persist, such as limited positional accuracy, inaccuracies in distance estimation, restricted operational range, sensor noise and distortion inherent in RGB-D data, environmental constraints (e.g., interference from sunlight), and complexities associated with sensor data fusion and calibration. IMU sensors are frequently employed as critical inputs for ML models, either directly or through fusion with other sensors [16]. Accurate navigation often relies on integrating IMU data, especially indoors. To enhance the positional accuracy and robustness of navigation systems for visually impaired users, advanced sensor fusion algorithms combining data from GNSS and inertial sources, including IMU, are commonly utilized [11; 18]. Two-dimensional codes are also a popular method for indoor navigation [19; 20; 21]. However, they require attaching printed codes to surfaces such as ceilings or floors. In practice, administrators of art galleries are often reluctant to install such markers, as they can detract from the gallery's aesthetic appearance. Despite this limitation, two-dimensional codes offer high positioning accuracy, low cost, and fast information retrieval [19; 20; 21]. Object detection constitutes a critical component in assistive navigation systems for individuals with visual impairments [22]. Various technologies demonstrate distinctive efficacy profiles when implemented in detection frameworks: Ultrasonic sensors offer pragmatic obstacle detection capabilities with favorable cost-efficiency ratios, though they exhibit operational range limitations that constrain their utility in complex navigational scenarios [23; 24]. Near Field Communication (NFC) technology presents advantages in market penetration and implementation economics but suffers from proximity constraintsand performance degradation in densely populated environments [12]. LiDAR technology demonstrates superior performance metrics in spatial resolution, continuous environmental scanning, and three-dimensional data acquisition. However, significant limitations manifest in temporal processing constraints, operational deficiencies in complex environmental topographies, and form factor integration challenges [11]. RGB-D camera implementations excel in generating semantic three-dimensional environmental mappings and facilitating real-time obstacle detection. These advantages are counterbalanced by substantial graphical processing requirements and computational dependencies on convolutional neural network architectures [18]. Traditional camera-based detection systems provide precision in object recognition, multi-object classification capabilities, and model specialization opportunities, while simultaneously requiring extensive training datasets, significant computational resources, and exhibiting vulnerability to detection anomalies [18; 22; 25]. Mobile device-based detection applications leverage integrated cameras for environmental sensing, typically transmitting captured data to server infrastructure for ML processing and subsequent generation of auditory feedback [26]. This methodology introduces potential communication latency issues, highlighting the critical importance of on-device processing capabilities to achieve real-time object detection performance necessary for effective assistive navigation.