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

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.

Three-dimensional attitude determination strategy for fused polarized light and geomagnetism

Using polarized light sensors to obtain only two-dimensional heading information does meet actual needs in navigation. Instead, an alternative method is proposed that uses the positional information of the Sun and geomagnetic information to calculate the three-dimensional attitude of a vehicle. First, the theoretical background of the polarization mode of skylight is described, and the scheme in using the atmospheric polarization pattern to calculate the solar position is presented. Second, the traditional three-axis attitude-determination (TRIAD) algorithm that exploits the solar position vector and the geomagnetic vector to obtain the three-dimensional attitude and the optimized TRIAD algorithm are introduced. Static and turntable experiments are described that verify the accuracy of the attitude calculation. Experimental results show that when using the optimized TRIAD algorithm, the root mean square errors for the roll angle, pitch angle, and heading angle are 0.1225°, 0.668°, and 1.0234°, respectively. This means that the optimized TRIAD algorithm performs significantly better than the traditional TRIAD algorithm and demonstrates that using the solar position and the geomagnetic information to obtain the three-dimensional attitude of the vehicle is very effective.

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.

Adaptive method for estimating information from a polarized skylight

The acquisition and processing of skylight polarization information forms the cornerstone in modern navigation systems that are developed by imitating certain biological mechanisms. The accuracy of skylight polarization mode information plays a major part in improving the accuracy of polarized light navigation. This paper mainly focuses on developing a methodology that can avoid the error caused by the inaccurate rotation of the polarizer and manual readings from non-electrical equipment, when the time-sequence polarization measurement system is used to obtain the skylight polarization mode information. We propose an adaptive algorithm that can obtain the pictures of angle of polarization and degree of polarization with sets of random rotation angles with no need for precise readings for the rotation angle of the polarizer. By allocating initial random values to rotation angles, a simple iterative estimation method like the Gaussian-Newton method can be used to converge calculated angle of polarization and degree of polarization values to their respective real values. The experiment results show that the proposed method can be used to estimate polarization information with high accuracy and universality under various experiment settings including both sunny and cloudy weathers. Meanwhile, the time efficiency of the proposed method is comparable to traditional methods.