LAUSR

An increasing number of bio-inspired navigation approaches have been designed based on polarization cameras. However, digital cameras can sense a much narrower field of vision than the vision of insects or human beings. In this study, we propose an adaptive skylight polarized orientation method for high dynamic range (HDR) scenes. Initially, we built a model of the image acquisition pipeline that can recover HDR irradiance maps from polarization images. Subsequently, the orientation method was designed based on a combination of the irradiance maps and the least squares methods. Some preprocessing steps were utilized to eliminate occlusion interference. In addition, an autoexposure adjustment method was proposed using information entropy and heuristic segmentation. Finally, the experimental results show that the proposed method can improve the accuracy of bionic orientation and adaption to skylight with occlusions and interference in natural conditions.