Determining the distances from the hull of the own ship to obstacles or land, i.e. water clearance, is a fundamental task in navigation. This is particularly relevant during maneuvering in the harbor or navigating in confined waters. We introduce the concepts of area water clearance and line water clearance. Area water clearance is important especially for path planning and obstacle avoidance. Line water clearance is critical for maneuvering when approaching the quay.In this work, we present a vision-based approach to determine the water clearance. A single calibrated camera together with a semantic segmentation network is used to detect the water region in an image, and back-projection to determine the water clearance on the sea surface in world units.We validate the proposed approach on real data collected from two distinct vessels, where the proposed method is able to produce reliable water clearance for distances beyond one kilometer. During harbor maneuvering 90% of the relative water clearance errors were found to be between −2.3% and 3%.

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.

Radar odometry may provide valuable input for surface vessels in several marine applications. The vulnerability of global positioning satellite systems to jamming and spoofing motivates the search for alternatives. In this work, we investigate the feasibility of W-band frequency modulated continuous wave radars in marine settings for odometry. A method to model radar resolution is presented and is further extend to include multiple radar frames. Numerical implementation relies on concepts from Lie theory. The proposed methods were evaluated on datasets collected from a ferry in a harbour area, where the average relative translation error was reduced to 3.07% compared to 14.2% of a baseline method.

Modern maritime navigation is heavily dependent on satellite systems. Availability of an accurate position is critical for safe operations, but satellite-based navigation systems are vulnerable to interference, jamming, and spoofing. In this work, we propose a method for maritime navigation independent of GNSS, able to provide absolute positioning of the vessel based on marine radar scans. A measurement model is presented where a Digital Elevation Model is used to predict the output of a marine radar, given a hypothetical position. The model, as used by an on-line particle filter, is used to track the movements of a ship from real recorded data. This demonstrates the feasibility of this method for robust positioning, without the need of external positioning signals, in a maritime environment. The tracking only uses sensors commonly available on maritime vessels, and demonstrates its application using freely available elevation data.