Bionic Polarized Skylight Orientation Method Based on the Model Consistency of Polarization Patterns in Cloudy Weather

Abstract

Skylight polarized patterns provide fresh thinking for the human to use the information sources in nature for orientation, but this orientation method is susceptible to weather conditions. Especially, in the case of cloudy weather, the cloud directly leads to the decline of orientation precision. In this article, a robust bionic polarized skylight orientation method based on the model consistency of polarization patterns (MCOPPs) is proposed for cloudy weather. First, we made full use of the DOP information to preprocess the polarization information as an aid to the subsequent polarization light orientation. Then, based on the vertical feature of the E-vector of the polarized light and the scattering plane, we developed the MCOPP error criterion and proposed a two-step optimizing framework for the effective selection of the interior points. Finally, the outdoor static rotating experiment and the dynamic vehicle experiment are conducted for evaluating our algorithm. The experimental results demonstrate that MCOPP can realize an effective and robust polarized orientation under cloudy conditions, and the accuracy of orientation in the outdoor static experiment is within 0.5° and in the dynamic car experiment is within 0.7°. Compared with the other four classic methods, the RMSE of the orientation error can be decreased by 50.6% in the outdoor static experiment and by 46.0% in the dynamic vehicle experiment. Especially, the proposed MCOPP has lower computational complexity than the existing robust polarized skylight method.