This paper presents Point Cloud Map Fitting (PCMF), a SLAM-based method for robust global positioning of autonomous surface vehicles in harbor environments. To address the challenge of precise positioning in unstructured and dynamic environments, a LiDAR sensor provides accurate measurements of the surroundings, which are fitted to a prior map using rotational initial alignment and G-ICP detail registration. The initial alignment process improves the accuracy of subsequent registration by leveraging corner features of the environment to address the limitations associated with large transformations in ICP registration. The resulting position estimates enable redundant positioning estimated by an Unscented Kalman Filter sensor fusion algorithm together with GNSS. The pipeline is tested in the field with multiple locations, tides, and weather conditions to demonstrate efficient position estimation in real time. The multimodal positioning system is stress-tested through the manipulation of GNSS signals in a simulated cyber-attack, which serves to demonstrate the edge case robustness of the system.