MSPCC: A Shipborne LiDAR Data Hierarchical Compression Approach in Dynamic Port Environment

Le résumé fourni par la source

The application of shipborne LiDAR has significantly enhanced the autonomous navigation capabilities of maritime autonomous surface ships (MASS) and provided a new solution for shore-based monitoring of ship dynamics. However, in port environments, the large volume and high sparsity of point cloud pose a major challenge to data transmission and storage, hindering efficient ship-to-shore information exchange and remote control. This paper proposes a hierarchical compression approach for navigation environment point cloud, combining deep learning with the PNG format. The approach first applies motion compensation to the measured data using the shipborne IMU, then transforms the unstructured environmental point cloud into a structured matrix, and finally performs hierarchical data compression on the feature images. Real-ship test results demonstrate that the reconstructed LiDAR data exhibits high quality and geometric fidelity, with the RMSE as 0.148m and the SNNRMSE as 0.1191m. The PSNR shows an average improvement of approximately 11.5%, while the average encoding time is only 150ms. This approach thus improves the compression efficiency of perception data during ship-shore interaction, while enhancing safety for MASS navigating in port waters.

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