Texture Camera Calibration via Depth Image Feature Matching
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Solution Overview
Problem
Depth camera systems face calibration challenges due to mechanical tolerances and environmental factors, leading to degradation in image quality over time, requiring costly and complex recalibration processes that are typically not user-friendly.
Innovation Solution
An apparatus and method for continuous and automatic calibration of texture cameras in depth camera systems, using an image receiver, feature extractor, misalignment detector, and calibrator to adjust calibration parameters, allowing for targetless and fully autonomous calibration without the need for special equipment or procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory calibration using high resolution cameras and calibration equipment is used, then initial calibration accuracy is improved, but recalibration becomes costly and complex
Solution Approach 1:
The system performs automatic self-calibration using its own depth camera and texture camera without requiring external calibration equipment. The depth camera captures calibration target images, and the system automatically computes calibration parameters through feature matching and optimization algorithms, enabling the device to calibrate itself without user intervention or specialized equipment.
Solution Approach 2:
The patent replaces the mechanical/optical calibration system (high resolution cameras and physical calibration equipment) with a computational system. Instead of using specialized hardware for calibration, the system uses the existing depth camera combined with image processing algorithms, feature extraction, and automatic optimization to achieve calibration, substituting physical calibration infrastructure with software-based solutions.
2Stability of the object's composition
If stringent mechanical tolerances are used to prevent decalibration, then calibration stability is improved, but manufacturing costs increase
Solution Approach 1:
The system transitions from static calibration (fixed mechanical tolerances) to dynamic calibration (continuous automatic recalibration). The calibration parameters can be updated at runtime based on environmental conditions and component drift, allowing the system to adapt to changes rather than relying on rigid mechanical precision maintained during manufacturing.
Solution Approach 2:
The patent changes the calibration parameters from fixed mechanical values determined at manufacturing to dynamically adjustable software parameters. The system stores calibration parameters in memory and can modify them through automatic calibration routines, allowing flexibility in manufacturing tolerances while maintaining operational accuracy through parameter adjustment rather than mechanical precision.
3Measurement precision
If traditional recalibration processes are used, then calibration accuracy can be restored, but user accessibility deteriorates
Solution Approach 1:
The calibration process is fully automated and executed by the system itself without requiring user knowledge or action. The depth camera automatically captures calibration images, the processor extracts features, computes misalignment, and adjusts calibration parameters autonomously, making the service self-performing and completely accessible to end users.
Solution Approach 2:
The patent extracts the complex calibration procedures from the user's responsibility and isolates them into automatic background processes. The cumbersome steps of manual calibration (setting up equipment, adjusting parameters, verifying results) are removed from user interaction and handled automatically by the system's image processing and optimization algorithms.
4Ease of operation
If manual calibration procedures are used, then calibration can be performed, but time consumption increases
Solution Approach 1:
The system enables continuous calibration capability, where the depth camera and processor can perform calibration operations continuously or at scheduled intervals without interruption. The automatic feature extraction, misalignment detection, and parameter optimization run continuously in the background, eliminating idle time between calibration operations and allowing immediate recalibration when needed.
Solution Approach 2:
The system performs preliminary calibration actions automatically during device initialization, setup, or idle periods before calibration drift becomes problematic. The depth camera captures calibration images and the processor computes calibration parameters in advance, preparing the system for optimal performance before actual use, thereby reducing the need for time-consuming recalibration during operational periods.
Data Source
AI summary
An example apparatus for calibrating texture cameras includes an image receiver to receive a depth image from a depth camera and a color image from a texture camera. The apparatus also includes a feature extractor to extract features from the depth image and the color image. The apparatus further includes a feature tester to detect that the extracted features from the depth image and the color image exceed a quality threshold. The apparatus includes a misalignment detector to detect a misalignment between the extracted features from depth image and the extracted features from color image exceeds a misalignment threshold. The apparatus also further includes a calibrator to modify calibration parameters for the texture camera to reduce the detected misalignment between the extracted features from the depth image and the extracted features from the color image below a misalignment threshold.