Regulatory Equivalence of Sensor-derived Models: Bridging Maritime Autonomous Navigation with LiDAR SLAM and S-100 Standards

Kurzfassung

Maritime Autonomous Surface Ships (MASS) rely on real-time perception to ensure safe and compliant navigation within dynamic maritime environments. However, regulatory frameworks require an “equivalent level of safety” to human-operated vessels without explicitly defining standards for validating autonomous situational awareness. This paper investigates whether LiDAR-based Simultaneous Localization and Mapping (SLAM) can generate sensor-derived environmental models that align with the geometric and functional safety expectations embedded in the S-100 and specifically the S-101 Electronic Navigational Chart (ENC) standards. We conduct field trials in a harbor and lock scenario using ICP-based SLAM, which is validated against high-precision Terrestrial Laser Scanning (TLS) ground truth to assess geometric accuracy, coverage completeness, structural consistency and topological fidelity to assess regulatory equivalence. Results suggest that SLAM-derived models can complement static ENCs by adding dynamic, situational information that can be audited, thereby addressing key elements of functional safety under IMO guidelines. We argue that integrating validated sensor-based perception as a complementary layer within the S-100 framework provides a viable path to certify MASS navigation systems, helping to connect sensor technologies with existing regulations.