A collaborative surface target detection and localization method for an unmanned surface vehicle swarm

Abstract

A single unmanned surface vehicle (USV) designed for marine missions suffers from limited payload, low efficiency and weak intelligence, while a swarm of USVs shows significant advantages in mission flexibility, diverse payload and task efficiency. One of the key issues for an USV swarm is how to achieve highly efficient collaborative perception. To address this issue, a method framework of collaborative surface target detection and localization based on multiple sensors for a swarm including 4 USVs is designed. First, perception systems are constructed, a joint calibration method for different sensors is proposed, and a lightweight target detection method improved with attention mechanism and lightweight adaptive spatial feature fusion is designed. Second, a specialized fusion method using sensor principles based on an extended Kalman filter (EKF) is proposed for a single USV to obtain a target state model. Third, the obtained target models from different USVs are registered with fuzzy matching and integrated into the complete model in a geographic coordinate system. The proposed method is applied to the collaborative perception system on our developed 4 USV swarm and verified in real marine environment and simulation. Experimental results show that our proposed method framework significantly improves the accuracy, efficiency, and reliability of the target detection and localization. The proposed LAF-YOLOv8-s reduces the model size by 5.1M, while the mean average precision (mAP) reaches 68.7%, which is significantly superior to other methods. The average collaborative localization error is reduced by 2.9m. The dataset is available at https://github.com/maochenyu1/WSLight.