# Tilt-Induced Error Compensation with Vision-Based Method for Polarization Navigation
However, it is environmentally sensitive, has subpar performance in dynamic environments, and only provides heading angle reference [6]. Celestial navigation, based on the passive reception of celestial radiation, demonstrates high autonomy and strong anti-interference capabilities. Nevertheless, star trackers are costly, and star pattern-matching algorithms require extensive computational resources with poor real-time performance [7]. Visual attitude measurement methods employ visual sensors to capture environmental images, identifying feature points or targets within the imagery to deduce UAV position and orientation [8; 9]. Visual sensors possess distinct benefits over conventional measurement techniques, including the capacity to capture rich environmental information, low power consumption, lightweight design, high cost-effectiveness, and strong adaptability [10]. Therefore, this study employs visual methods for horizontal attitude angle estimation and utilizes the estimated attitude angle information to correct tilt-induced errors in polarized light navigation. Table 1 provides a comparative overview of the aforementioned attitude estimation methods. In the field of visual attitude measurement, prevalent methodologies encompass artificial marker-based approaches, natural feature-based methods, multi-view geometry techniques, and optical flow algorithms. [PERSON] et al. [11] addressed the autonomous landing of multi-rotor UAVs on moving vehicles by introducing a novel landing pad design and its recognition algorithm, achieving relative pose estimation at elevated sampling rates. However, the landing accuracy of their method exhibited a mean error of approximately 40 cm. [PERSON] et al. [12] proposed a semantic segmentation-based horizon detection method, achieving horizon gradient errors around \(1^{\circ}\). However, it demands high computational resources and time for training and inference. [PERSON] et al. [13] developed an innovative horizon detector that employs RGB video streams, improving detection precision in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Application Scenario** & **Advantages** & **Limitations** \\ \hline Inertial Navigation & Used for short periods & Autonomy, High-frequency updates & Error accumulation, High cost \\ \hline Satellite Navigation & Outdoor open areas & Global coverage, Real-time & Reliant on signals, Susceptible \\  & & performance & to interference \\ \hline Geomagnetic & & Low power consumption, & Sensitive to \\ navigation & Magnetic field stability region & Hidden susceptibility & environmental interference \\ \hline Celestial Navigation & Open sky visibility, maritime, & High precision, no external & Expensive, High \\  & aerial navigation & dependence, long-lasting & computational complexity \\ \hline Visual Navigation & Rich environmental scenarios & No reliance on signals, High mobility, & Affected by lighting changes, \\  & & Low cost & Vulnerable to \\  & & Low cost & optical distortions \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of Application Scenarios and Performance of Attitude Estimation Methods. low-contrast conditions; yet its accuracy is affected by temporal information accumulation. [PERSON] et al. [14] presented a SLAM (Simultaneous Localization and Mapping) framework that accommodates monocular, stereo, and visual-inertial sensors, achieving high-precision attitude tracking through ORB (Oriented FAST and Rotated BRIEF) feature matching and Bundle Adjustment optimization, demonstrating sensitivity to texture-deficient environments. [PERSON] et al. [15] achieved attitude estimation without accelerometers using motion models and optical flow, but their algorithm's roll and pitch angle variances were 1.24\({}^{\circ}\) and 0.84\({}^{\circ}\), respectively, and performance degraded during hover or low-speed conditions. [PERSON] et al.