Color Depth Sensor Calibration Using Planar Object Alignment
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Solution Overview
Problem
Calibrating color cameras and depth sensors is challenging due to differences in their coordinate systems and the inability to analyze colored patterns in depth images, leading to unreliable boundary points and noisy depth images.
Innovation Solution
The calibration involves synchronizing images of a planar object, such as a checkerboard, captured by both cameras, and computing rotation and translation between their coordinate systems using techniques like fitting planes or randomly sampling points in the depth image, allowing for accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If colored patterns are used for calibration, then color camera calibration is improved, but depth image analysis becomes impossible
Solution Approach 1:
The calibration process is segmented into two independent parts: color camera calibration using colored patterns in color images, and depth sensor calibration using geometric features in depth images. This allows each sensor to be calibrated using its optimal method without interference from the other sensor type's limitations.
Solution Approach 2:
A calibration object containing both colored patterns and geometric features serves as an intermediary that provides calibration information for both color cameras and depth sensors simultaneously. The colored patterns enable color camera calibration while the geometric features enable depth sensor calibration, resolving the contradiction between the two calibration needs.
2Ease of operation
If boundary points are used for calibration, then object segmentation is simplified, but depth reliability deteriorates
Solution Approach 1:
Different regions of the calibration object serve different purposes: colored patterns provide reliable depth information for intrinsic calibration, while geometric features provide reliable boundary information for extrinsic calibration. This local differentiation of function allows each part to contribute its strength without being compromised by the weaknesses of other parts.
3Measurement precision
If external infrared illumination is used, then infrared image alignment is improved, but device complexity increases
Solution Approach 1:
The calibration object contains geometric features that are inherently detectable by the depth sensor without requiring external infrared illumination. The depth sensor uses its own active illumination or ambient light to capture the geometric features, allowing the system to be self-sufficient and eliminating the need for additional external infrared light sources.
4Measurement precision
If multiple image pairs are captured, then calibration accuracy is improved, but calibration time increases
Solution Approach 1:
The calibration object is pre-designed with known geometric features and colored patterns at fixed positions and orientations. This preliminary preparation allows the calibration algorithm to quickly identify and match features across multiple image pairs without requiring time-consuming manual measurements or adjustments, thus reducing calibration time while maintaining accuracy.
Data Source
AI summary
A system described herein includes a receiver component that receives a first digital image from a color camera, wherein the first digital image comprises a planar object, and a second digital image from a depth sensor, wherein the second digital image comprises the planar object. The system also includes a calibrator component that jointly calibrates the color camera and the depth sensor based at least in part upon the first digital image and the second digital image.