The Imaging Polarization Sensor (IPS) can provide high-accuracy, robust, and fully autonomous navigation information for underwater unmanned platforms. However, the polarization images in complex underwater scenarios are affected by dynamic-wave interference, severely impacting the accuracy of underwater polarization orientation. Therefore, a suppression method of dynamic-wave interference based on angular increment assistance is proposed. Firstly, the transmission model of polarized light under dynamic-wave interference is constructed, and the error mechanism of the Angle of Polarization (AOP) is analyzed. Then, combining the inertial angular increment, a total differential increment model of AOP under the condition of carrier motion is established and used as the theoretical prior for the relative change in adjacent frames. On this basis, an AOP deviation estimation method based on complementary filtering is proposed, which exploits the complementary characteristics between the total differential increment of AOP and the actual detected values, achieving dynamic-wave suppression with single-frame input and thereby improving the accuracy of polarization orientation. In actual underwater experiments, the proposed method not only effectively improves the quality of AOP images but also significantly enhances the orientation accuracy. Compared to the existing optimal methods, the underwater static orientation error and the underwater dynamic orientation error are reduced by 26.3% and 33.4%, respectively.

The bionic navigation system composed of polarization sensor (PS)-aided inertial measurement unit could provide high-accuracy attitude and heading for unmanned vehicles in GNSS rejection or magnetic interference environments. However, the existing loosely coupled method is vulnerable to occlusion interference and atmospheric depolarization. Therefore, this paper proposes the tightly coupled attitude and heading measurement method based on full-sky polarization mode. Firstly, the tightly coupled system model is constructed, in which the sun azimuth error and zenith error are added to the state vector, and the angle of polarization (AOP) deviation of multiple observation channels are taken as the measurement. On this basis, a robust fusion mechanism based on the sequential cubature Kalman filter is proposed. And the linear gravity measurement update and the nonlinear polarization measurement update are carried out in turn to improves the system robustness. In the rotation test, the slight interference vehicle test, and the serious interference vehicle test, the heading RMSE of the proposed method compared with that of the loosely coupled method are reduced by 32.5%, 32.3%, and 70.2% respectively, which verifies the great advantages in practical unmanned driving applications.

Relative pose measurement for noncooperative objects is an important part of 3D shape recognition and motion tracking. The methods based on scanning point clouds have better environmental adaptability and stability than image-based methods. However, the discrete points obtained from a continuous surface are sparse, which leads to point-to-point dislocations in the overlapping area and seriously reduces the accuracy. Therefore, this paper proposed a relative-pose-measurement algorithm based on double-constrained intersurface mutual projections. First, the initial corresponding set was constructed using mutual projections between the areas with similar feature descriptors, and then the final corresponding set was determined through the rigid-transformation-consistency constraint to improve the accuracy of the matchings and achieve a high-accuracy relative pose measurement. In the Stanford dataset, the rotation error and translation error were reduced by 19.3% and 13.4%, respectively. Furthermore, based on the proposed evaluation method, which separated the error of the pose-measurement algorithm from that of the instrument, the experiments were carried out with a self-made swept-frequency interferometer. The rotation error was reduced by 39.8%, and the surface deviation was reduced by 4.9%, which further proved the advancement of the method.

Direct sunlight in complex environmental conditions severely interferes with the light intensity response for imaging Polarization Sensor (PS), leading to a reduction in polarization orientation accuracy. Addressing this issue, this article analyzes the impact mechanism of direct sunlight on polarization sensor detection in a complex environment. The direct sunlight interference factor is introduced into the intensity response model of imaging polarization detection, enhancing the accuracy of the polarization detection model. Furthermore, a polarization state information analytical solution model based on direct sunlight compensation is constructed to improve the accuracy and real-time performance of the polarization state information solution. On this basis, an improved bio-orientation method based on direct sunlight compensation for imaging polarization sensor is proposed. The outdoor dynamic reorientation experiment platform is established to validate the effectiveness of the proposed method. Compared with the traditional methods, the experimental results demonstrate a 23% to 47% improvement in the polarization orientation accuracy under various solar zenith angles.