论文标题
探索天窗两极化模式是否包含三维态度信息
Exploration of Whether Skylight Polarization Patterns Contain Three-dimensional Attitude Information
论文作者
论文摘要
我们以前的工作表明，在极化天窗导航中广泛使用以描述天窗极化模式的瑞利模型不包含三维（3D）姿态信息[1]。但是，仍然有必要进一步探索天窗极化模式是否包含3D态度信息。因此，在本文中，提出了一种社交蜘蛛优化（SSO）方法来估计三个Euler角度，该角度考虑了基于模板匹配（TM）的极化图像之间每个像素的差异，以充分利用捕获的极化信息。此外，为了探索这个问题，我们不仅使用极化角度（AOP）和极化程度（DOP）信息，还使用光强度（LI）信息。因此，建立了一个天空模型，该模型结合了浆果模型和Hosek模型，以完全描述天空中的AOP，DOP和LI信息，并考虑了四个中性点，地面反照率，大气浊度和波长的影响。模拟的结果表明，SSO算法可以估计3D态度，并且已建立的天空模型包含3D态度信息。但是，当存在测量噪声或模型误差时，3D态度估计的精度会大大下降。尤其是在现场实验中，很难估计3D态度。最后，将详细讨论结果。
Our previous work has demonstrated that Rayleigh model, which is widely used in polarized skylight navigation to describe skylight polarization patterns, does not contain three-dimensional (3D) attitude information [1]. However, it is still necessary to further explore whether the skylight polarization patterns contain 3D attitude information. So, in this paper, a social spider optimization (SSO) method is proposed to estimate three Euler angles, which considers the difference of each pixel among polarization images based on template matching (TM) to make full use of the captured polarization information. In addition, to explore this problem, we not only use angle of polarization (AOP) and degree of polarization (DOP) information, but also the light intensity (LI) information. So, a sky model is established, which combines Berry model and Hosek model to fully describe AOP, DOP, and LI information in the sky, and considers the influence of four neutral points, ground albedo, atmospheric turbidity, and wavelength. The results of simulation show that the SSO algorithm can estimate 3D attitude and the established sky model contains 3D attitude information. However, when there are measurement noise or model error, the accuracy of 3D attitude estimation drops significantly. Especially in field experiment, it is very difficult to estimate 3D attitude. Finally, the results are discussed in detail.
