Cavitation Detection and Depth Measurement Based on SLAM Mapping and Multi-Frame Point Cloud Stitching

This paper proposes a method for cavitation detection and absolute depth measurement based on Simultaneous Localization and Mapping (SLAM) mapping technology, combined with multi-frame point cloud feature stitching. Cavitation is a common form of wear in mechanical equipment, typically manifesting as small pits or cracks on the material surface. Long-term cavitation can severely affect the performance and lifespan of equipment. Therefore, accurate detection of cavitation defects is crucial for maintaining and safely operating industrial equipment. In this study, SLAM technology is used to obtain precise camera pose information. Through feature extraction and matching of multi-frame point cloud data, high-quality 3D point clouds are generated. By analyzing the stitched point clouds, we can accurately identify the location, shape, and severity of cavitation defects, and provide quantitative data through absolute depth measurement. This method offers a new approach to cavitation detection with broad application prospects.