Structured Light Camera-Projector Misalignment Correction
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Solution Overview
Problem
Structured light systems face misalignment issues due to unintentional changes in the relative position of the camera and projector, especially in non-stationary applications, which require frequent recalibration and are not practical with existing techniques that rely on fixed patterns and flat surfaces.
Innovation Solution
An automatic calibration method that extracts features from captured images, matches them to predetermined patterns, and applies an image alignment transformation model to correct misalignment, allowing for continuous operation without manual intervention or changes in projected patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration with fixed patterns and flat surfaces is used, then initial alignment accuracy is achieved, but the system requires frequent recalibration and cannot adapt to non-stationary conditions
Solution Approach 1:
The calibration system transitions from static fixed patterns to dynamic scene-based features. The system continuously extracts and matches features from captured images against a database of scene features, enabling real-time adaptation to changing relative positions between camera and projector in handheld or mobile applications.
Solution Approach 2:
The system performs self-calibration by automatically extracting features from captured scene images and matching them to the database without requiring manual intervention or external calibration objects. The processor autonomously determines transformation parameters and updates the mapping between camera and projector coordinate systems.
2Stability of the object's composition
If the camera-projector pair remains stationary with fixed calibration, then calibration stability is maintained, but the system cannot handle unintentional position changes over time
Solution Approach 1:
The system continuously captures images, extracts features, and compares them against the database to detect position changes. Based on this feedback, the processor automatically updates transformation parameters to maintain accurate depth estimation despite unintentional movements of the camera-projector pair.
Solution Approach 2:
A comprehensive database of scene features is pre-established and stored in memory before operation. This preliminary preparation enables rapid feature matching and calibration updates without requiring real-time complex computations or external calibration objects when position changes occur.
3Measurement precision
If frequent recalibration is performed to maintain accuracy in non-stationary conditions, then alignment precision is preserved, but system productivity and continuous operation are reduced
Solution Approach 1:
The calibration process becomes continuous and seamless during operation. Feature extraction and matching occur in real-time on captured images without requiring system shutdown or manual intervention, enabling continuous depth estimation and 3D reconstruction in handheld or mobile applications.
Solution Approach 2:
The system replaces manual mechanical calibration procedures with automated computational methods. Image processing algorithms automatically extract features, match them to the database, and compute transformation parameters, eliminating the need for physical calibration objects and manual adjustment operations.
Data Source
AI summary
A method of misalignment correction in a structured light device is provided that includes extracting features from a first captured image of a scene, wherein the first captured image is captured by an imaging sensor component of the structured light device, and wherein the first captured image includes a pattern projected into the scene by a projector component of the structured light device, matching the features of the first captured image to predetermined features of a pattern image corresponding to the projected pattern to generate a dataset of matching features, determining values of alignment correction parameters of an image alignment transformation model using the dataset of matching features, and applying the image alignment transformation model to a second captured image using the determined alignment correction parameter values.