Manufacturing Component Detection With 3D Point Cloud Segmentation
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Solution Overview
Problem
Machine-learning routines in manufacturing environments require large amounts of training data and time to accurately perform component detection, leading to inefficiencies in automated assembly tasks.
Innovation Solution
A method and system for segmenting point cloud data into clusters, filtering production component cloud data, aligning with predefined axes, classifying asset types, and generating three-dimensional bounding boxes to identify and determine parameters of production components, utilizing techniques like convolutional neural networks and point-in-polyhedron routines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning routines are used for component detection, then detection accuracy is improved, but training time and data requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing point cloud data through segmentation into clusters, filtering to identify production component cloud data, and aligning with predefined axes before classification. This preparation work is done in advance to reduce the computational burden during actual detection, thereby reducing training time while maintaining accuracy
Solution Approach 2:
The point cloud data is segmented into multiple clusters based on spatial distribution and characteristics. Each cluster is processed independently through filtering and alignment operations, which breaks down the complex detection task into manageable segments. This segmentation approach reduces overall processing time while preserving detection accuracy
2Measurement precision
If machine-learning routines are used for component detection, then detection accuracy is improved, but data requirements increase
Solution Approach 1:
The system performs preliminary filtering and alignment operations on point cloud data before classification. By pre-processing the data to remove non-production components and align with predefined axes, the system reduces the amount of raw data that needs to be processed during training, thereby reducing data requirements while maintaining detection accuracy
Solution Approach 2:
The system extracts and isolates production component cloud data from the broader point cloud dataset through selective filtering. By separating production components from non-production components before classification, the system reduces the quantity of data that needs to be processed and stored, while focusing computational resources on relevant components for accurate detection
3Measurement precision
If complex processing routines are used for component detection, then detection precision is improved, but system complexity increases
Solution Approach 1:
The complex detection process is segmented into distinct modules: point cloud segmentation into clusters, filtering to identify production components, alignment with predefined axes, and classification. Each module performs a specific function with well-defined inputs and outputs, making the overall complex system more manageable and easier to implement while maintaining high detection precision
Data Source
AI summary
A method includes segmenting point cloud data of an image into a plurality of input clusters, wherein each of the plurality of input clusters includes a given set of point cloud data from among the point cloud data. The method includes, for each of the plurality of input clusters: selectively filtering the given set of point cloud data to identify production component cloud data (PCCD), aligning the PCCD with a predefined axis to generate a rotated PCCD, classifying the given set of the plurality of production components into one or more asset types based on the rotated PCCD, generating a three-dimensional bounding box based on the rotated PCCD, segmenting the rotated PCCD into a plurality of production clusters, and determining one or more parameters associated with the given set of the plurality of production components based on the plurality of production clusters and the one or more asset types.