Study on skylight polarization patterns over the ocean for polarized light navigation application

Passive Polarized Vision for Autonomous Vehicles: A Review

This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

Image-registration-based solar meridian detection for accurate and robust polarization navigation

Skylight polarization, inspired by the foraging behavior of insects, has been widely used for navigation for various platforms, such as robots, unmanned aerial vehicles, and others, owing to its stability and non-error-accumulation. Among the characteristics of skylight-polarized patterns, the angle of polarization (AOP) and the degree of polarization (DOP) are two of the most significant characteristics that provide abundant information regarding the position of the sun. In this study, we propose an accurate method for detecting the solar meridian for real-time bioinspired navigation through image registration. This method uses the AOP pattern to detect the solar meridian and eliminates the ambiguity between anti-solar meridian and solar meridian using the DOP pattern, resulting in an accurate heading of the observer. Simulation experiments demonstrated the superior performance of the proposed method compared to the alternative approaches. Field experiments demonstrate that the proposed method achieves real-time, robust, and accurate performance under different weather conditions with a root mean square error of 0.1° under a clear sky, 0.18° under an overcast sky with a thin layer of clouds, and 0.32° under an isolated thick cloud cover. Our findings suggest that the proposed method can be potentially used in skylight polarization for real-time and accurate navigation in GPS-denied environments.

Speedy bearings to slacked steering: Mapping the navigation patterns and motions of Viking voyages

Viking sailors ruled the North Atlantic Ocean for about three hundred years. Their main sailing route was the 60° 21' 55'' latitude between Norway and Greenland. Although they did not have a magnetic compass, in sunshine they used a sun-compass to determine the geographical north (solar Viking navigation: SVN). It has been hypothesized that when the Sun was invisible, Viking navigators determined the direction of polarization of skylight with sunstones (dichroic/birefringent crystals), and then estimated the geographical north using the sun-compass (sky-polarimetric Viking navigation: SPVN). Many details of the hypothetical SPVN have been thoroughly revealed in psychophysical laboratory and planetarium experiments. Combining these results with measured celestial polarization patterns, the success of SPVN was obtained as functions of sailing, meteorological and navigation parameters (sunstone type, sailing date, navigation periodicity, night sailing, cloudiness conditions). What was so far lacking in this experimental and computational archeological approach is the study of the success of SVN and a combined navigation using solar cues in sunshine (SVN) and sky polarization at invisible Sun (SPVN), the latter being the most realistic method. In this work we determine the success of the sole SVN and the combined SVN-SPVN relative to the mere SPVN for three navigator types (determining the intended sailing direction with large, medium or small frequencies) at spring equinox and summer solstice, with and without night sailing. We found that to maximize the sailing success, navigators had to choose different navigation methods depending on the navigation frequency. Using sky polarization with very frequent navigation, resulted in the highest chance to survive a three-week voyage from Norway to Greenland.

Polarized light compass decoding

The brains of some insects can encode and decode polarization information and obtain heading angle information. Referring to the encoding ability of insects, exponential function encoding is designed to improve the stability of the polarized light compass artificial neural network. However, in the decoding process, only neurons with the largest activation degree are used for decoding (maximum value decoding), so the heading information contained in other neurons is not used. Therefore, average value decoding (AVD) and weighted AVD are proposed to use the heading information contained in multiple neurons to determine the heading. In addition, concerning the phenomenon of threshold activation of insect neurons, threshold value decoding (TVD) and weighted TVD are proposed, which can effectively eliminate the interference of neurons with low activation. Moreover, this paper proposes to improve the heading determination accuracy of the artificial neural network through pre-training. The simulation and experimental results show that the new, to the best of our knowledge, decoding methods and pre-training can effectively improve the heading determination accuracy of the artificial neural network.

Polarization reflection distribution characteristics of wakes on the sea surface

Research on the polarization reflection distribution characteristics of wakes on the sea surface can provide a theoretical basis for ocean wake target detection and has important research value in the field of ship and underwater moving target monitoring. The Kelvin wake model and the Cox-Munk model are used to describe a wake on a rough sea surface. Considering the atmospheric Rayleigh scattering and the reflection characteristics of a rough sea surface, a visible spectrum band wake polarization characteristic model based on the Stokes vector and Mueller matrix is established to explore the polarization reflection distribution characteristics of wakes on the sea surface under skylight background at different wind speeds, wind directions, and sun angles. A simulation is done of the airborne polarization reflection imaging of wakes on a rough sea surface. The results show that under the determined observation angle, the polarization distribution characteristics of wakes on a rough sea surface are mainly related to the angle of the sun. The polarization contrast of simulated wakes in typical scenes is acceptable, and it is feasible to detect sea wake targets by the polarization method. The analysis and simulation of the wake polarization characteristics model can provide a theoretical basis for ocean wake target detection.

Polarized light sun position determination artificial neural network

Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized light solar position determination (PLSPD), which has two branches: the solar azimuth angle determination branch and the solar elevation angle determination branch. Since the solar elevation angle has no cyclic characteristics, and the angle range of the solar elevation angle is different from that of the solar azimuth angle, the solar elevation angle exponential function encoding is redesigned. In addition, compared with the PLOD, the PLSPD deletes a local full connection layer to simplify the network structure. The experimental results show that the PLSPD can determine not only the solar azimuth angle but also the solar elevation angle, and the solar azimuth angle determination accuracy of the PLSPD is higher than that of the PLOD.

Bio-inspired attitude measurement method using a polarization skylight and a gravitational field

High precision and reliability attitude measurement play an important role in autonomous unmanned navigation. Finding inspiration from desert ants, known as highly efficient navigators who can find their way after foraging for hundreds of meters from their home in hostile environments, we propose an attitude measurement method using polarization skylight and gravitational field. Contrary to the previous method, we utilize three-dimensional polarization vectors and any one-dimensional output of the accelerometers to calculate attitudes. In addition, we designed an accelerometer component selection algorithm, which is to select the one-dimensional component with the minimum motion acceleration from the output of the three-dimensional accelerometer. With this method, even if the carriers remain in a maneuvering state, the motion acceleration of the vehicle will have less impact on the accuracy of attitude measurement. To evaluate the performance of our method, the outdoor experiment was carried out to compare our method with existing traditional methods. Comparison results show that our method has higher measurement accuracy than others and is still applicable in the case of carriers maneuvering in practice under a clear sky.

Polarized skylight compass based on a soft-margin support vector machine working in cloudy conditions

The skylight polarization pattern, which is a result of the scattering of unpolarized sunlight by particles in the atmosphere, can be used by many insects for navigation. Inspired by insects, several polarization navigation sensors have been designed and combined with various heading determination methods in recent years. However, up until now, few of these studies have fully considered the influences of different meteorological conditions, which play key roles in navigation accuracy, especially in cloudy weather. Therefore, this study makes a major contribution to the study on bio-inspired heading determination by designing a skylight compass method to suppress cloud disturbances. The proposed method transforms the heading determination problem into a binary classification problem by segmentation, connected component detection, and inversion. Considering the influences of noise and meteorological conditions, the binary classification problem is solved by the soft-margin support vector machine. In addition, to verify this method, a pixelated polarization compass platform is constructed that can take polarization images at four different orientations simultaneously in real time. Finally, field experimental results show that the designed method can more effectively suppress the interference of clouds compared with other methods.