Orientation and position information are indispen~able in mobile robot navigation. In this paper, a novel polarization sensor for getting orientation information is introduced. It exports an absolute azimuth angle. To take it effect in mobile robot navigation, a fuzzy logic controller is designed. It can direct the robot walking along the designed trajectory according to the information from the polarization sensor. Assisted by the wheel encoders, some navigation experiments are implemented outdoors. The result indicates that the error of the polarizatioñ ensor is independent of the travel distance and it can play an Important role in automatic navigation system in a further way.

The quality of underwater polarization imaging is mainly affected by the polarization properties of the target and impurity particles. Traditional methods often assume uniform polarization characteristics of the target, which make it difficult to address the restoration issues of complex targets. In response, a partition-based method for recovering underwater polarization images is proposed. The method involves preprocessing the image using the Gaussian curvature filtering algorithm, partitioning the image based on polarization information. In addition, a joint image evaluation method is used to achieve restoration of complex polarized characteristic targets. The method estimates the value of the reflected light polarization of one partition to estimate the value of the next partition and links the polarization values of each partition. Our approach achieves clear restoration results for multiple targets or complex structural objects underwater. Achieving significant improvement in image quality in multi-target underwater scenes, our method is highly effective for complex underwater environments. Experimental results show that our method, when compared with three other newer methods on multiple images of different targets and under varying scattering conditions, achieves an average increase of 617% in the standard deviation image evaluation index and a 61% optimization in the natural image quality evaluator index. Furthermore, our method is robust for different degrees of water turbidity.

This article aims to reduce the influence of heavy fog on the outdoor imaging equipment and to maximally improve the foggy image resolution. It is known that reflected light of the object includes the abundant target polarization information. The foggy image can be restored by these information of the target and the airlight. Therefore, this article introduces a multi-channel polarization information system and defogging algorithm accordingly. The polarization information system provides a necessary solution for the accurate application of a specific algorithm, since it can ensure the accuracy of acquired image information. The key point of the proposed algorithm lies in accurately estimating the parameters in the polarization defogging model. Based on the normal threshold distribution comparison, it can avoid the highlight portion of the non-sky area in the image and accurately estimate the airlight intensity at infinite distance so as to effectively reduce the color distortion of the bright area. The median filtering algorithm is used to obtain the airlight degree of polarization by using three obtained polarization scenes. At last, this article analyzes the experimental results through defogging evaluation indexes and compared the result obtained by this algorithm with others.

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