Automated Volume Quantification of Deck-Loaded Riprap from Portable LiDAR SLAM Point Clouds

Accurate quantification of riprap volume is critical for cost control, quality assurance, and navigation safety in inland waterway maintenance projects. Conventional methods, such as draft mark reading and RTK-based point surveying, are constrained by limited accuracy, low efficiency, and operational risk. To address these limitations, this study proposes a fully automated riprap volume quantification method based on portable LiDAR simultaneous localization and mapping. The proposed framework establishes a seamless, intervention-free workflow. This automated process sequentially integrates real-time scan monitoring, target vessel extraction, riprap segmentation, deck baseline reconstruction, and 3D volume estimation. Specifically, riprap-laden transport vessels are automatically identified using density-based clustering and trajectory information. Subsequently, deck-loaded riprap piles are extracted through point-cloud geometric analysis and quantified via deck fitting and mesh reconstruction. The method was validated through ten field experiments in the Jingjiang reach of the middle Yangtze River, China. Compared to benchmark volumes established via standard point-cloud processing software, the proposed method achieved an average relative error of 1.37% and a maximum error strictly below 5%. Furthermore, the system proved highly efficient, requiring an average processing time of only 392.1 s per dataset. The results demonstrate that the proposed method is accurate, efficient, and robust, and has strong potential for intelligent riprap quantification in inland waterway engineering.