Real-time Imaging Orientation Determination System to Verify Imaging Polarization Navigation Algorithm

Solar position detection method by bionic polarized light compass

To address the needs of polarized light navigation for accurate position information of feature points in the sky, an accurate solar position detection method based on an all-sky polarization pattern imaging system is proposed. Unlike the traditional spot-based solar position detection method, this method uses the polarization information inherent in the atmosphere to accurately measure solar position. This approach is characterized by simple detection, high accuracy, and wide application range. The optical acquisition system is composed of three miniature large-field camera modules and polarizers, which enables a more compact structure, smaller size, and lesser height. Based on this principle, the solar position solution algorithm was simulated and then verified in various weather environments using the optical acquisition system built as part of this study. Solar position was detected at different moments on the same day in clear weather, and the accuracy of the measured solar altitude and azimuth angles was 0.024° and 0.03°, respectively. The accuracy of the measured solar altitude and azimuth angles was 0.08° and 0.05°, respectively, when the sun was shielded by high-rise buildings and 0.3° and 0.1° when the sun was shielded by branches and tree leaves. Aerosol concentrations exceeding a certain amount destroyed the Rayleigh distribution pattern of polarized light, thus affecting solar position detection accuracy. It is concluded that this novel detection method can not only meet the needs of polarized light navigation for solar position, but also provide a new exploration idea for enthusiasts who are eager to explore the mysteries of the universe.

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.

A bio-inspired polarization navigation sensor based on artificial compound eyes

Insect compound eyes are optical systems with small volume and a compact structure. The ommatidia in the dorsal rim area of some insects have polarized vision, which can perceive the polarization pattern of the sky and provide them with navigation information. In this paper, inspired by the polarization-sensitive compound eyes of insects, a bio-inspired polarization navigation sensor based on artificial compound eyes is designed. The sensor consists of an artificial compound eye, an integrated polarization detector and an integrated circuit. The optical path of the sensor uses the lens defocus method, which can ensure that the sensor obtains redundant polarization information. The integrated polarization detector is used to obtain the polarization information of the incident light, and the integrated circuit is responsible for the calculation. To extract effective information from images, we propose a multi-threshold segmentation method to filter and classify effective pixels. We use the least squares method to fit the inherent error of the sensor and then compensate it. The indoor calibration accuracy of the sensor is ±0.3°, and the outdoor calibration accuracy is ±0.5°. The sensor can provide accurate direction information for general smart mobile devices. The size of the sensor is 4 × 4 × 2 cm, and the weight is only 15 g. The key components of the sensor can be mass-produced, and it is a miniaturized and low-cost polarization navigation sensor.

Optical Design of a Common-Aperture Camera for Infrared Guided Polarization Imaging

Polarization and infrared imaging technology have unique advantages for various applications ranging from biology to ocean remote sensing. However, conventional combined polarization camera and infrared camera have limitations because they are constrained to single-band imaging systems with rotating polarizers and cascaded optics. Therefore, we propose a common-aperture mode based on multi-band infrared guided polarization imaging system (IGPIS) in this paper, which consists of infrared wide-area sensing and polarization features acquisition for accurate detection of ship targets. The IGPIS can provide images in visible polarization (0.45–0.76 μm), near-infrared polarization (0.76–0.9 μm), and long-wave infrared (8–12 μm) bands. Satellite attitude parameters and camera optical parameters are accurately calculated by establishing a dynamic imaging model for guidance imaging. We illustrate the imaging principle, sensors specifications and imaging performance analysis and the experimental results show that the MTF is 0.24 for visible and near-infrared, and 0.13 for long-wave infrared. The obtained multi-band images have an average gradient of 12.77 after accurate fusion. These results provide theoretical guidance for the design of common-aperture cameras in remote sensing imaging field.

A 3D Attitude Estimation Method Based on Attitude Angular Partial Feedback for Polarization-Based Integrated Navigation System

Polarization (POL) navigation is inspired by insects’ behavior of precepting celestial polarization patterns to orient themselves. It has the advantages of being autonomous and having no accumulative error, which allows it to be used to correct the errors of the inertial navigation system (INS). The integrated navigation system of the POL-based solar vector with INS is capable of 3D attitude determination. However, the commonly used POL-based integrated navigation system generally implements the attitude update procedure without considering the performance difference with different magnitudes of the angles between the solar-vector and body-axes of the platform (S-B angles). When one of the S-B angles is small enough, the estimated accuracy of the attitude angle by the INS/POL is worse than that of the strapdown inertial navigation system. To minimize the negative impact of POL in this situation, an attitude angular adaptive partial feedback method is proposed. The S-B angles are used to construct a partial feedback factor matrix to adaptively adjust the degree of error correction for INS. The results of simulation and real-world experiments demonstrate that the proposed method can improve the accuracy of 3D attitude estimation compared with the conventional all-feedback method for small S-B angles especially for yaw angle estimation.

Limitation of Rayleigh sky model for bioinspired polarized skylight navigation in three-dimensional attitude determination

Insects such as desert ants and drosophilae can sense polarized skylight for navigation. Inspired by insects, many researchers have begun to study how to use skylight polarization patterns for attitude determination. The Rayleigh sky model has become the most widely used skylight polarization model for bioinspired polarized skylight navigation due to its simplicity and practicality. However, this is an ideal model considering only single Rayleigh scatter events, and the limitation of this model in bio-inspired attitude determination has not been paid much attention and lacks strict inference proof. To address this problem, the rotational and plane symmetry of the Rayleigh sky model are analyzed in detail, and it is theoretically proved that this model contains only single solar vector information, which contains only two independent scalar pieces of attitude information, so it is impossible to determine three Euler angles simultaneously in real-time. To further verify this conclusion, based on a designed hypothetical polarization camera, we discuss what conditions different three-dimensional attitudes must satisfy so that the polarization images taken at different 3D attitudes are the same; this indicates that multiple solutions will appear when only using the Rayleigh sky model to determine 3D attitude. In conclusion, due to its single solar vector information and the existence of multiple solutions, it is fully proved that 3D attitude cannot be determined in real time based only upon the Rayleigh sky model. Code is available at: https://github.com/HuajuLiang/HypotheticalPolarizationCamera.

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

Polarized skylight navigation has excellent navigation performance with no error accumulation over time and low susceptibility to interference. The skylight polarization distribution contains rich directional information, such as the solar meridian, the neutral point, and the polarization angle, which plays a key role in the polarization navigation. But up to now the polarizations of both sunlit and moonlit skies have been investigated mainly over the land. In this work, the polarization distribution patterns of the skylight over the East China Sea and the Yellow Sea were studied. The polarization patterns were captured continuously during daytime and nighttime by using a full-sky imaging polarimetry system and then compared with the simulation results using the libRadtran radiative transfer software package. The result shows that the skylight polarization distribution over the sea has almost the same pattern as that on the land. The accuracy of the angle of polarization and the degree of polarization dropped significantly under the cloudy sky. It was found that when the ship sailed on the sea, the direction of the real meridian was close to the solar azimuth during the daytime and close to the lunar azimuth during the nighttime. It was also found that the nautical twilight polarization distribution was affected by both the solar polarization and the lunar polarization, but the solar polarization was dominant. The experiments show that the skylight polarization distribution pattern over the sea can still be applied in the field of polarization navigation. Thus, it is feasible for ships and unmanned aerial vehicles to use the polarized skylight to navigate and orient on the sea.

Real-time polarization imaging algorithm for camera-based polarization navigation sensors

Biologically inspired polarization navigation is a promising approach due to its autonomous nature, high precision, and robustness. Many researchers have built point source-based and camera-based polarization navigation prototypes in recent years. Camera-based prototypes can benefit from their high spatial resolution but incur a heavy computation load. The pattern recognition algorithm in most polarization imaging algorithms involves several nonlinear calculations that impose a significant computation burden. In this paper, the polarization imaging and pattern recognition algorithms are optimized through reduction to several linear calculations by exploiting the orthogonality of the Stokes parameters without affecting precision according to the features of the solar meridian and the patterns of the polarized skylight. The algorithm contains a pattern recognition algorithm with a Hough transform as well as orientation measurement algorithms. The algorithm was loaded and run on a digital signal processing system to test its computational complexity. The test showed that the running time decreased to several tens of milliseconds from several thousand milliseconds. Through simulations and experiments, it was found that the algorithm can measure orientation without reducing precision. It can hence satisfy the practical demands of low computational load and high precision for use in embedded systems.