Abstract
In this paper, we propose a real time underwater sonar-inertial system for pipeline reconstruction. In our approach, the sonar data is preprocessed by the likelihood method, from which we calculate the optimal estimation of obstacles position. The cluster center, feature vector and the feature point of sonar point cloud are computed by our cluster-based method, the feature information are utilized to state estimate and 3D reconstruction. During the state estimation step, an IEKF (iterative extended Kalman filter) are used to fuse IMU and sonar data, which are suitable for real-time calculation on the embedded platforms. The proposed method is validated in underwater experiment, the result shows our method have great performance in real-time and accuracy, in which process time can achieve 53 ms and relative translation error can be reduced to 2.5%. The 3D point cloud reconstructed in our method is shown at the end of article.
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Gong, W., Xiao, L., Zeng, T., Li, Y., Sun, Z., Wang, Z. (2022). SOINS: A Real-Time Underwater Sonar-Inertial System for Pipeline 3D Reconstruction. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_25
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DOI: https://doi.org/10.1007/978-3-031-13835-5_25
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