Rock Fragmentation Assessment Using 3D Point Cloud Scaling
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Solution Overview
Problem
Existing image-based rock fragmentation analysis methods face challenges in accurately assessing fragmented materials, particularly when access to the site is restricted or when scaling reference objects are difficult to place, and they lack effective methods for determining slope stability and safety.
Innovation Solution
A method and apparatus that utilize two-dimensional image data and three-dimensional point locations to identify features, determine dimensional attributes, and assess slope stability, incorporating 3D sensors and image sensors to generate a 3D point cloud, allowing for accurate scaling and slope orientation analysis, and generating warnings for unsafe working conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference objects are introduced into the region of interest for scaling, then measurement precision is improved, but device complexity and ease of operation deteriorate due to restricted access and difficulty in placing reference objects
Solution Approach 1:
The patent uses 3D point cloud data as an intermediary to establish scale without requiring physical reference objects in the region of interest. The 3D point cloud serves as a mediator between the imaging system and the measurement system, allowing accurate scaling through computational geometry rather than physical placement of reference objects.
Solution Approach 2:
The patent replaces the mechanical system of physically placing and measuring reference objects with an optical-computational system. Instead of using physical reference objects that require manual placement and measurement, the system uses image processing and 3D point cloud analysis to computationally determine scale and dimensions.
2Measurement precision
If parallel laser beams are projected onto the region of interest for scaling, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent extracts the scaling function from complex laser beam projection systems and integrates it into standard image processing workflows. By using 3D point cloud data that can be obtained from various sensors (stereoscopic cameras, LiDAR, structured light), the system removes the need for specialized laser projection equipment while maintaining accurate scaling capabilities.
Solution Approach 2:
The patent creates a universal scaling approach that works with multiple types of 3D sensing devices (stereoscopic image sensors, LiDAR, structured light sensors) rather than being tied to a specific laser projection system. This multi-functional approach allows the same image processing pipeline to handle different sensor types and measurement scenarios.
3Measurement precision
If 3D point cloud data is used for dimensional analysis, then measurement precision and ease of operation are improved, but loss of information increases due to data processing complexity
Solution Approach 1:
The patent segments the 3D point cloud data into meaningful features and boundaries corresponding to individual rock fragments or material objects. By dividing the complex point cloud into discrete, identifiable segments that match visual features, the system reduces data processing complexity while maintaining precise dimensional measurements of each segment.
Data Source
AI summary
A method and apparatus for performing a fragmentation assessment of a material including fragmented material portions is disclosed. The method involves receiving two-dimensional image data representing a region of interest of the material, and processing the 2D image data to identify features of the fragmented material portions. The method also involves receiving a plurality of three dimensional point locations on surfaces of the fragmented material portions within the region of interest, identifying 3D point locations within the plurality of three dimensional point locations that correspond to identified features in the 2D image, and using the identified corresponding 3D point locations to determine dimensional attributes of the fragmented material portions.