SLAM for Autonomous Docking: A Case Study of Copenhagen's Harbour Buses

Autonomous docking, particularly for harbour buses, requires precise localization and mapping to ensure reliable operation. This paper presents the implementation of a SLAM solution designed to ensure accurate localization and mapping for Copenhagen's harbour buses while enabling reliable detection of docking platforms. The proposed approach utilizes data collected from the harbour bus route and leverages the FAST-LIO2 algorithm and a detection module based on point cloud registration. The system's accuracy is assessed using RTK measurements and GNSS data. The results demonstrate that FAST-LIO2 can achieve centimeter-level precision in both localization and mapping. Additionally, the use of RANSAC registration with a docking station template proves effective for precise localization within the generated maps. Moreover, a proof-of-concept experiment confirms that integrating relocalization within the FAST-LIO2-generated maps can further enhance the system's reliability.