A Novel Attitude Determination System Aided by Polarization Sensor

Abstract

This paper aims to develop a novel attitude determination system aided by polarization sensor. An improved heading angle function is derived using the perpendicular relationship between directions of E-vector of linearly polarized light and solar vector in the atmospheric polarization distribution model. The Extended Kalman filter (EKF) with quaternion differential equation as a dynamic model is applied to fuse the data from sensors. The covariance functions of filter process and measurement noises are deduced in detail. The indoor and outdoor tests are conducted to verify the validity and feasibility of proposed attitude determination system. The test results showed that polarization sensor is not affected by magnetic field, thus the proposed system can work properly in environments containing the magnetic interference. The results also showed that proposed system has higher measurement accuracy than common attitude determination system and can provide precise parameters for Unmanned Aerial Vehicle (UAV) flight control. The main contribution of this paper is implementation of the EKF for incorporating the self-developed polarization sensor into the conventional attitude determination system. The real-world experiment with the quad-rotor proved that proposed system can work in a magnetic interference environment and provide sufficient accuracy in attitude determination for autonomous navigation of vehicle.

A Review: The Survey of Attitude Estimation in Autonomous UAV Navigation

“…For the estimation of UAV speed and position, the computational accuracy of factor graph filtering algorithm was higher than that of extended KF (EKF) method. In [11] a UAV attitude determination system based on polarization sensor was developed. Because the polarization sensor is not affected by the magnetic field, the system can work in the magnetic interference environment.…”

“…In [6] the estimate of UAV location and orientation was calculated in the EKF relies on inertial measurements and information extracted from the aerial imagery. In [11] the quaternion differential equation was adopted as the dynamic model to improve EKF, and the improved algorithm integrates data detected by the polarization sensor to improve the positioning accuracy of UAV. In [12] the adaptivity of KF was improved with INS and UWB data in UAV moving were fused to improve positioning accuracy.…”

Insect-inspired vision for autonomous vehicles

“…3b, composed of just a single unit, which was recently mounted on board a quadrotor (Fig. 3d), gave promising performances: indoor accuracy 0.2 • , outdoor accuracy less than 2 • , and output refresh signal 10 Hz [64]. A polarization-based photodetector involving a bilayer nanowire was recently developed and tested under artificial blue lighting, giving a mean error of only ±0.1 • once a polynomial fitting process had been applied [7].…”

“…In addition, this insect-based compass, which is highly reliable and suitable for performing navigation tasks in various meteorological contexts [15], is able to determine its heading angle with great precision: the median error recorded was only 2.9 • when the sky was slightly cloudy, and 1.9 • in the case of an overcast sky [16]. Several artificial celestial compass sensors have been produced for robotic and autonomous navigation purposes [17,34,35,64].…”

Sensor Modeling and Calibration Method Based on Extinction Ratio Error for Camera-Based Polarization Navigation Sensor

“…In general, the polarization sensors can be divided into two types: polarization-sensitive type (POL-type) sensor and camera-based polarization sensor. The POL-type sensor simulates the signal process in the polarization opponent neurons in ommatidium of insects [ 10 , 11 , 12 , 13 ]. Although the POL-type sensor is able to generate real-time navigation measurements with less computational complexity, it can be severely affected by the cloudy sky, the tree occlusion, and other complex outdoor sky conditions [ 14 , 15 , 16 ].…”