Polarized Skylight Navigation Simulation (PSNS) Dataset

With more and more machine learning methods applied to bioinspired polarized skylight navigation, the demand for polarized skylight navigation datasets is more and more urgent, which can be used to train and test machine learning methods. So, in this paper, an open polarized skylight navigation dataset is constructed for the first time. Firstly, a polarized sky model was proposed based on Sun position model, Berry sky model and Hosek sky model, which contains the information of the light intensity (LI), degree of polarization (DOP) and angle of polarization (AOP). Secondly, a polarization imaging simulation system is constructed, which can capture not only LI, DOP and AOP images, but also original black-and-white LI images in different polarization directions. Black-and-white LI images are the original data collected by actual polarization imager, so this system can completely describe the whole process of polarization imager capturing skylight polarization patterns. Above all, a polarized skylight navigation simulation (PSNS) dataset can be constructed. In addition, to facilitate researchers to build their own datasets based on their own polarized light sensors and sky models, we have disclosed the source code of polarization imager and original LI imager on GitHub

Exploration of Whether Skylight Polarization Patterns Contain Three-dimensional Attitude Information

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

Influence of sensor tilts on bio-inspired polarized skylight orientation determination

Inspired by many insects, several polarized skylight orientation determination approaches have been proposed. However, almost all of these approaches always require polarization sensor pointing to the zenith of the sky dome. So, the influence of sensor tilts (not point to the sky zenith) on bio-inspired polarization orientation determination needs to be analyzed urgently. Aiming at this problem, a polarization compass simulation system is designed based upon solar position model, Rayleigh sky model, and hypothetical polarization imager. Then, the error characteristics of four typical orientation determination approaches are investigated in detail under only pitch tilt condition, only roll tilt condition, pitch and roll tilts condition respectively. Finally, simulation and field experiments all show that the orientation errors of four typical approaches are highly consistent when they are subjected to tilt interference, in addition, the errors are affected by not only the degree of inclination, but also the solar altitude angle and the relative position between the Sun and polarization sensor. The results of this paper can be used to estimate the orientation determination error caused by sensor tilts and correct this kind of error