3D Point Cloud Discontinuity Detection on Pipe Surfaces
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Solution Overview
Problem
Existing methods for identifying discontinuities on pipe surfaces, such as welds or joints, struggle when the diameter difference is insignificant or the discontinuity's position is non-repetitive, making it difficult to precisely determine the robot's trajectory.
Innovation Solution
A method using a 3D vision system to reconstruct the scene as a point cloud, filter the data, and analyze the orientation of normals to identify discontinuities through triangulation and depth mapping, combined with a system that includes a digitizing sensor and a processing unit to guide a robotic arm for precise work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated systems use diameter difference analysis or fixed distance calculation with reference objects to identify discontinuities, then the system can detect discontinuities, but the measurement precision deteriorates when diameter difference is not significant or position is not repetitive
Solution Approach 1:
The patent transitions from 2D vision systems to 3D vision systems, utilizing three-dimensional point cloud data to represent the pipe surface. This dimensional change enables the system to capture depth information and surface geometry, allowing discontinuity detection based on 3D spatial characteristics rather than relying solely on diameter differences or fixed reference distances, thereby improving measurement precision in challenging cases
Solution Approach 2:
The patent replaces traditional mechanical measurement approaches with optical 3D vision systems. Instead of using physical probes or relying on manual inspection, the system uses laser-based or structured light-based 3D scanning to capture the pipe surface geometry, enabling non-contact, high-precision discontinuity detection that overcomes the limitations of diameter-based methods
2Adaptability or versatility
If manual inspection methods are used by human operators, then flexibility in inspection is maintained, but productivity decreases due to manual operation
Solution Approach 1:
The system enables automated self-inspection of pipe surfaces through 3D vision systems and autonomous discontinuity detection algorithms. The system independently captures 3D data, processes point cloud information, identifies discontinuities, and generates inspection reports without human intervention, thereby maintaining the flexibility of detecting various discontinuity types while dramatically improving inspection productivity and speed
3Measurement precision
If 3D vision systems are implemented to reconstruct the scene as a point cloud, then measurement precision improves for discontinuity detection, but device complexity increases
Solution Approach 1:
The patent extracts and focuses on the essential 3D geometric features from the complete point cloud data, specifically isolating surface normal vectors and their orientation characteristics. By concentrating on these key extracted features rather than processing the entire raw point cloud, the system achieves high measurement precision while reducing computational complexity and making the device more manageable
Data Source
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AI summary
A method for identifying a discontinuity on a surface of an object placed within a work environment is described. The method comprises digitizing the work environment to obtain a 3D point cloud; determining, within the work environment, a search volume comprising the discontinuity and extracting from the point cloud the points belonging to the search volume; considering the points of the point cloud within the search volume, determining the normals to the surfaces defined by the points; determining, for each of the normals, its orientation; and analyzing the orientation of the normals in the search volume to identify the discontinuity in the presence of an anomalous variation in the orientation of the normals.