Visual-Inertial Localization Leveraging Skylight Polarization Pattern Constraints

In this letter, we develop a tightly coupled polarization-visual-inertial localization system that utilizes naturally-attributed polarized skylight to provide a global heading. We introduce a focal plane polarization camera with negligible instantaneous field-of-view error to collect polarized skylight. Then, we design a robust heading determination method from polarized skylight and construct a global stable heading constraint. In particular, this constraint compensates for the heading unobservability present in standard VINS. In addition to the standard sparse visual feature measurements used in VINS, polarization heading residuals are constructed and co-optimized in a tightly-coupled VINS update. An adaptive fusion strategy is designed to correct the cumulative drift. Outdoor real-world experiments show that the proposed method outperforms state-of-the-art VINS-Fusion in terms of localization accuracy, and improves 22% over VINS-Fusion in a wooded campus environment.