An obstacle detection method for dual USVs based on SGNN-RMEN registration of dual-view point clouds

Abstract

This paper proposes an unsupervised obstacle detection method for dual unmanned surface vessels (USVs) using the SGNN-RMEN framework. The method utilizes dual-view point clouds captured by two USVs to improve the detection of small obstacles under the condition of a visible shore. Firstly, a siamese graph neural network (SGNN) is designed to extract global contour features. A specialized task of global contour consistency classification is employed to ensure the interpretability of these features. Secondly, a rotation matrices estimation network (RMEN) is utilized to estimate the optimal rotation transformation for aligning point clouds based on the global contour features of the dual-view point clouds. The overlap degree between the dual-view point clouds before and after registration is used as feedback to train the model, enabling unsupervised learning. Finally, a " gridding - filtering - clustering” method is applied to annotate the size and position of obstacles based on the registration of the dual-view point clouds. Comparative experiments on two datasets demonstrate the effectiveness of the proposed method in accurately extracting contour features, achieving precise dual-view point cloud registration, and improving the detection rate of small obstacles in nearshore environments. The method also exhibits strong adaptability in unfamiliar marine environments.