Autonomous Surface Vehicles (ASVs) must operate safely in dynamic and often cluttered maritime environments such as ports and inland waterways. Achieving reliable situational awareness in these settings remains challenging due to the limited availability of annotated datasets, the complexity of multi-sensor alignment, and the degradation of monocular depth estimation at long range. This thesis addresses these challenges by developing a multi-modal 3D object detection framework that combines radar and camera data to improve perception robustness and depth accuracy.
To support supervised learning and evaluation, a custom dataset was constructed using real-world maritime sensor data, including radar, camera, and navigation information. Three-dimensional object annotations were manually created to capture static and moving targets across a wide range of distances.
Other core contributions include a probabilistic radar association strategy that models uncertainty in sensor measurements, a learnable Radar Gaussian Parameter Network (RGPNet) for dynamic estimation of radar association parameters, and a depth-aware mechanism for dynamically adjusting the region used for radar–camera fusion.
These methods are designed to improve object localisation performance, particularly in conditions where sensor data is sparse, noisy, or spatially misaligned.
The proposed models were evaluated through a series of controlled experiments examining detection performance across different depth intervals and clutter conditions. The results show that probabilistic radar fusion improves robustness to noise and depth estimation errors, while depth-aware association strategies enhance localisation at long range without sacrificing near-field precision. Incorporating adaptive mechanisms into the fusion process was shown to be particularly effective in scenarios involving large depth variation or limited visual information.
Overall, the findings demonstrate that uncertainty-aware, adaptive fusion methods can significantly improve 3D object detection performance for ASVs. The approaches developed in this work offer a robust foundation for future research on scalable, data-efficient maritime perception systems for autonomous navigation in complex environments.