How to self-determine the attitude and heading of users is a challenging issue in unknown, global navigation satellite system (GNSS) denied, or magnetic disturbance environments. In this article, a bioinspired polarization-based attitude and heading reference system (PAHRS) aided by inertial sensors is presented. As compared with the existing studies, a polarization compass, which consists of three photoelectric-based polarization sensors is designed to cope with the three-dimensional polarization heading determination and distinguish issue. Hence, the real heading orientation can be determined without GNSS correction. To address the triaxis attitude and heading determination issue of PAHRS, the system measurement model tightly coupled the attitude and heading accumulate errors of inertial navigation system. Finally, the experimental platform is built and the results illustrate that the PAHRS is able to achieve satisfactory performance without the aid by GNSS and magnetic compass system.

Stable and accurate heading angle estimation is challenging for low-cost small unmanned aerial vehicle (UAV) navigation, especially in the presence of geomagnetic anomaly. In this article, a bioinspired integrated navigation system (BINS) is designed such that the environmental adaptability of the navigation system can be enhanced. A bioinspired integrated navigation algorithm is presented to fuse the information of polarized skylight, geomagnetic field, inertia, and global navigation satellite system. The relationship among ambient light intensity, degree of polarization, and accuracy of polarization compass is revealed. Thus, the integrated polarization compass can be adapted to more complex weather conditions. A tightly coupled integrated navigation model is developed for BINS to fuse the information of polarization compass and magnetometer. Within the developed scheme, the polarization compass can not only correct the heading error of the system, but also help the magnetometer reconstruct the actual local geomagnetic field distribution. The problem of inaccurate geomagnetic field model in the magnetic anomaly environment is addressed. Moreover, the Chi-Square test is used to detect polarization compass outliers such that the navigation modes can be switched promptly. Finally, the ground static experiments and UAV flight tests are carried out. In comparison to traditional polarization navigation and geomagnetic navigation, the proposed system effectively improves the heading accuracy.

How to determine the absolute position of users, such as UAVs, ships or other long-range vehicles, is a fascinating and challenging issue, especially in GNSS-denied environments. In this paper, a bio-inspired global autonomous positioning system based on polarized skylight is proposed. By comparing with the related literatures, the system is able to global positioning based on the degree of polarization pattern (DoP) pattern. To be more specific, in an attempt to use the DoP pattern for the geolocalization purposes, a bio-inspired polarization compass is designed and in addition, an overall maximum degree of polarization (MDoP) determination algorithm is presented. Moreover, on the basis of the MDoP value, the sun elevation angle can be ultimately measured by using the DoP pattern. The sun elevation angle difference algorithm is used to deduce the absolute position of users. Finally, the bio-inspired global autonomous positioning system is developed and validated through experiments. The test results show that the location errors are in range of few kilometers, of which the latitude accuracy is 0.113° (1σ), and the longitude accuracy is 0.082° (1σ).

A novel autonomous initial alignment for the stationary strapdown inertial navigation system (SINS) with the bio-inspired polarized skylight sensors (PSS) is proposed to improve the precision and convergence speed in this paper. A pair of perpendicularly polarized skylight sensors with six-channels are used to measure and calculate the polarization azimuth angles, which is finally constructed a new polarized measurement error equation. With the SINS error equations, the SINS/PSS integrated navigation error equations are developed to achieve autonomous and fast initial alignment. Two types of observability, geometric observability and stochastic observability, are introduced to evaluate the integrated navigation systems. The geometric observability, which is defined by calculating the rank of the observability matrix, is analysed. It is found that the integrated navigation systems are not completely observable. Further, the degree of observability based on singular value decomposition is also used to examine the observable degree of the SINS/PSS integrated navigation system. The stochastic observability is associated with observability analysis with the estimated precision of the designed filter. The Kalman filter is designed to estimate the unknown state based on the measurements from the inertial measurement unit and PSS. Simulations and experiments demonstrate that the SINS/PSS achieve a rapid initial alignment with higher precision. Compared with the SINS, the vertical misalignment angle accuracy of SINS/PSS is improved by more than 90%, and the time cost is shortened by 60% in the simulation test. For the initial alignment experiment, the vertical misalignment greatly improved the accuracy and shorten the time cost.

For the outdoor scenes with sparse features, the performance of inertial navigation system (INS)/Lidar odometry (LO)-based simultaneous localization and mapping (SLAM) pose estimation is drastically affected by the accumulated drift error and sparse features. A polarized camera (Polcam) that can obtain the absolute yaw angle from the polarized skylight is introduced to compensate for the accumulated drift error. A yaw measurement model based on the polarized camera is derived, and it will not suffer from the sparse and GNSS-denied environment. Moreover, it is integrated with INS/LO to construct a factor graph. The factor graph that consists of INS factor, polarized camera factor, lidar odometer factor, and motion model factor is optimized by nonlinear least squares. For the sparse environmental features, a robust and effective method named correntropy-based bidirectional generalized ICP (CoBigICP) has been adopted to register the sparse point cloud data. A loosely coupled INS/LO/Polcam integration based on factor graph optimization for sparse scenes is proposed, and it is tested and validated by an open dataset and experiment in the real-world environment, respectively. Different integration modes with different register methods are compared both in the simulation and experiment. The results show that the INS/LO integration with Polcam via factor graph optimization can realize better pose estimation accuracy than without in a sparse environment.