Vehicle heading estimation using skylight polarization improves accuracy
Navigation by Skylight Polarization via Rayleigh Model Optimization on a Road Vehicle
Robotics
Summary
Navigating vehicles accurately can be tricky when GPS, magnets, or movement sensors fail or become unreliable. The authors developed a method that uses patterns of polarized light in the sky, caused by the sun’s rays scattering in the atmosphere, to figure out which way a vehicle is pointing. Their system compares real sky measurements to a computer model to find the best match and thus the vehicle’s direction. Tests on cars in urban areas showed this approach can estimate direction with less than one degree error and handle obstacles blocking parts of the sky better than older methods.
What this means in practice
- •For automotive navigation teams: Improve vehicle heading estimation when GPS or compass signals are weak or blocked using skylight polarization patterns.
- •For outdoor robotics developers: Use skylight polarization-based heading to maintain orientation for robots operating in environments with partial sky occlusion where traditional sensors struggle.
Authors
Ben Potter, Eslam Mounier, Aboelmagd Noureldin, Yahia Antar, Muhammad Alam
Abstract
Reliable absolute heading estimation is critical for road-vehicle navigation, particularly when magnetic, inertial, or GNSS-based references are degraded. This paper presents a skylight polarization navigation method that estimates vehicle heading by globally optimizing a Rayleigh sky model against measurements from a division-of-focal-plane polarimetric camera. The proposed framework combines a pinhole camera model, a single-scattering Rayleigh polarization model, and a solar position algorithm to synthesize the expected full image angle of polarization distribution for candidate vehicle states. A degree of polarization weighted root mean square error objective function compares measured and simulated polarization patterns, allowing the estimator to use all available sky information while attenuating low confidence pixels caused by clouds, buildings, foliage, and other depolarizing occlusions. Experimental evaluation is performed using three urban road vehicle trajectories collected with a roof mounted polarimetric camera and a high precision dual-antenna GNSS/INS reference system. Results show sub-degree RMS yaw error across all trajectories, ranging from $0.718^{\circ}$ to $0.889^{\circ}$. Compared with representative feature-based and model-based polarization navigation methods, the proposed approach reduces RMS heading error by up to 87% and demonstrates improved robustness to partial sky occlusion.