## 1 Introduction and Related Research
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indoor Positioning Technology** & **Principle** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Magnetic} & Position is determined by detecting & \multirow{2}{*}{Robustness in indoor  fingerprint recognition.} & Readings can vary between \\  & and analyzing changes in the  Earth’s magnetic field and & & \\  & surrounding magnetic materials. & & \\ \hline \multirow{4}{*}{Inertial} & Uses accelerometers and & \multirow{2}{*}{Suitable for indoor  environments without  GPS signals.} & Susceptible to cumulative errors,  sensor drift; require calibration \\  & & & \\ \hline \multirow{4}{*}{Sound} & Analyzes sound propagation, time  delay, and intensity to determine  position in indoor environments. & Adaptable to complex  environments, low cost,  unaffected by  lighting conditions. & Signal attenuation over long  distances or obstacles,  affecting accuracy. \\ \hline \multirow{4}{*}{Optical} & Uses optical sensors or cameras to  capture light signals (visible or  infrared) to determine position. & Infrared and VLC methods for  positioning; adaptable to  complex scenarios. & Sensitive to lighting, reflection,  and occlusion; may fail in  poor lighting. \\ \hline \multirow{4}{*}{Radio Frequency} & Uses RF signals, including Wi-Fi,  BLE, RFID, and UWB, to transmit  signals for indoor positioning. & Good accuracy and real-time  performance; penetrates walls  and obstacles. & Prone to signal interference,  multipath effects, and  deployment complexity. \\ \hline \multirow{4}{*}{Vision} & Uses fixed or mobile camera  systems to capture objects and  determine position via  landmark detection. & Provides detailed spatial data  using image features. & Poor performance in dark  environments,  computationally intensive. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of indoor positioning technologies. learning from large datasets. However, these models often struggle with occluded or distant objects and unfamiliar obstacle types. On the other hand, motion-based approaches such as background subtraction, optical flow analysis, and inter-frame differencing are effective in dynamic environments but typically provide only two-dimensional localization and suffer from limited depth perception, especially over greater distances. Indoor positioning and obstacle recognition, though traditionally studied as separate research areas, share numerous technical challenges and environmental constraints. Both require reliable operation in complex, dynamic indoor settings and depend heavily on visual feature extraction, particularly in the absence of external signals such as GPS or RF. Indoor positioning focuses on global positioning, while obstacle recognition emphasizes local awareness. However, both must be robust to occlusion, lighting changes, and material reflections. Recent advances in texture-based methods such as LBP and FFT have demonstrated cross-domain applicability, enhancing both positioning accuracy and object discrimination. The IMU module, which includes an accelerometer, gyroscope, and magnetometer, provides acceleration, angular velocity, and magnetic field data. This fusion enhances system performance under low-light, occluded, or visually degraded environments. Moreover, inertial-visual fusion techniques help mitigate sensor drift in localization while reducing noise and motion blur in recognition tasks. These shared requirements and synergistic techniques motivate the development of unified algorithms that can serve both functions. In our earlier work, we introduced the LRA, which establishes a logarithmic relationship between the laser irradiated area of spots and actual distance [10]. Experimental results demonstrated a minimum error of 1.6 cm and an average error of 2.4 cm within a 3 m range. However, since the algorithm only leverages local features from images, it lacks the capability to differentiate between background surfaces and actual obstacles, especially when material reflectivity varies under different lighting conditions.