Structured Light Measurement Without Triangulation Calibration
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Solution Overview
Problem
Conventional imaging systems for determining object characteristics require complex computations and calibration procedures, necessitating precise knowledge of spatial 3D relationships between camera, laser emitting device, and ground surface, which is time-consuming and often requires dedicated equipment, and recalibration if positional relationships change.
Innovation Solution
The method involves interpolating object characteristics using stored associations between known values and representations of laser line projections, eliminating the need for complex computations and calibration procedures by determining object characteristics based on relative 3D positions and orientations of the camera and laser device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If triangulation computations are used to determine object characteristics, then measurement precision is improved, but device complexity and calibration requirements increase
Solution Approach 1:
The system performs preliminary calibration by capturing images of training objects with known characteristics and storing the associations between laser line representations and characteristic values in a lookup table. This preliminary action eliminates the need for complex triangulation computations during actual measurement, as the system directly queries the pre-computed associations.
Solution Approach 2:
The system creates a computational model by capturing and storing representations of laser lines on training objects with known characteristics. These stored representations serve as reference copies that enable direct comparison and measurement without requiring repeated complex geometric computations.
2Reliability
If precise 3D relationships are established during manufacturing, then measurement reliability is improved, but manufacturing complexity and time increase
Solution Approach 1:
The system performs self-calibration by automatically capturing images of training objects, computing the associations between laser line representations and known characteristics, and populating the lookup table without requiring external calibration equipment or manual intervention. This self-service approach reduces both calibration time and manufacturing complexity.
3Measurement precision
If dedicated calibration equipment is used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system uses simple training objects with known characteristics as calibration references instead of expensive dedicated calibration equipment like checkerboard plates. These training objects serve as disposable or reusable references that enable accurate measurement without requiring sophisticated calibration tools.
4Ease of operation
If simple relative geometry is enforced between camera and laser emitter, then ease of operation is improved, but adaptability decreases
Solution Approach 1:
The system performs preliminary calibration for each specific configuration by capturing images of training objects and storing configuration-specific associations in the lookup table. This preliminary action enables the system to adapt to different camera-laser geometries without requiring simple fixed relationships, as each configuration is pre-characterized and stored.
Data Source
AI summary
The techniques described herein relate to methods, apparatus, and computer readable media for measuring object characteristics by interpolating the object characteristics using stored associations. A first image of at least part of a ground surface with a first representation of a laser line projected onto the ground surface from a first pose is received. A first association between a known value of the characteristic of the ground surface of the first image with the first representation is determined. A second image of at least part of a first training object on the ground surface with a second representation of the laser line projected onto the first training object from the first pose is received. A second association between a known value of the characteristic of the first training object with the second representation is determined. The first and second association for measuring the characteristic of a new object are stored.