Polarized Light Compass-Aided Visual-Inertial Navigation Under Foliage Environment

This paper presents a method to extract heading information from the sky light polarization pattern and successfully implements the polarized light compass (PL-compass) under foliage environment with heavy occlusions for the first time. A classifier based on the primal support vector machine is trained to detect the sky area, and the random sample consensus algorithm is used to choose the inliers among the sky area. The sun vector is built upon the valid measurements. The attitude angles (Roll and Pitch) from the inertial navigation system (INS) are used to establish the Zenith direction when the platform tilts during traveling. The results show that the PL-compass can provide accurate heading angle (RMSE = 1.58°) by seeing only a tiny part of the skylight (minimum of 0.028%) in the dynamic vehicle test. The PL-compass is also integrated with the visual INS based on the indirect extended Kalman filter to improve the navigation accuracy. Each time a new measurement becomes available, the states estimated by the INS are used to check the consistency of the innovation and reject the outliers. The results show that the position errors of the integrated navigation system are greatly reduced with the help of the PL-compass. The estimated trajectory tracks the ground truth very well. The position error is 25.9 m (1.03% of the distance traveled) with heading errors less than 1° in the experiment.