Filippo Ponzini

Reliable obstacle detection and classification are essential capabilities for the safe and efficient navigation of Marine Autonomous Surface Ships. This paper introduces a decision-level multi-sensor fusion framework to enhance situational awareness for autonomous vessels by integrating RGB camera and LiDAR data. Visual information is processed using a pre-trained, open-source object detection model. At the same time, LiDAR measurements are analysed with a clustering-based algorithm, followed by a lightweight Random Forest classifier for semantic labelling. To support practical deployment in real maritime environments, the proposed approach relies on readily available perception modules, avoiding the need for training on proprietary datasets and limiting dependence on extensive task-specific tuning. The fusion of these complementary sources is employed to confirm and characterise dynamic obstacles, whose positions derived from LiDAR are continuously tracked using a Global Nearest Neighbour algorithm supported by a Kalman filter. Each stage of the proposed processing chain is thoroughly described and experimentally validated using real-world data collected in a representative marine environment, demonstrating the approach’s effectiveness in improving perception performance by reducing false positives from noisy measurements and achieving 92% track number accuracy in a complex scenario.

1. With the rise of containerisation and the emergence of the offshore oil & gas industry in the 1960s, the ocean has become a hub of economic activities, which have experienced an exponential grow...

Attitude estimation is a popular topic in marine engineering and robotics; the position and orientation of a vehicle are required as feedback from several control algorithms to improve autonomous navigation capabilities, such as dynamic positioning, track keeping, and autodocking. Typically, position and heading angles are provided by the Global Positioning System and compass. Usually, during the development and testing, the experiments are performed in a controlled environment, such as an indoor test tank. However, Global Positioning System systems can be unreliable due to non-negligible model scale errors or the absence of line-of-sight with the satellites. This article presents an experimental tracking system setup suitable for indoor testing facilities. In particular, the paper presents a tracking system based on a GigE camera and ArUco markers detection and a LiDAR-based tracking system relying on unsupervised machine learning techniques. The MQTT broker-based publish/subscribe message-queuing protocol allows real-time data communication and sharing. The proposed system was developed, installed, and tested in the COMPASS laboratory (University of Genoa). The two tracking systems’ outcomes have been compared. Eventually, an accuracy analysis was performed by comparing the results to the ground truth in purpose-built experiments. The proposed approach can estimate the degrees of freedom of a self-propelled model-scale vessel in an indoor testing facility without requiring active or powered markers and share the information acquired with multiple entities in real-time at a high frame rate.