Marine Vessel Attitude Estimation from Coastline and Horizon

Reliable monitoring of vessel motions is crucial for safe and efficient operation of marine vessels. Pitching and rolling motions are commonly monitored using high-grade inertial measurement units (IMUs). However, such sensors become unreliable in presence of long-lasting accelerations. In this work, we propose a method for attitude estimation of marine vessels relying on an image stream and known world features. Our focus is on the estimation of pitch and roll angles. We employ a semantic segmentation network and process its output for robust extraction of coastlines and horizon. The image features are matched with known world features to estimate the attitude. The proposed method is validated using different metrics on data acquired from a small passenger ferry. The proposed method achieves more than 60% reduction in vertical reprojection error compared to IMU. We show that the proposed method outperforms IMU and can be used to replace it whenever horizon or coastline is visible.