ToF Camera Calibration via Lowpass Filtering
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Solution Overview
Problem
Conventional calibration methods for time-of-flight cameras combine Fixed Pattern Phase Noise (FPPN) and wiggling errors, making it difficult to achieve optimal accuracy in distance measurements.
Innovation Solution
A method involving digital lowpass-filtering of ToF images to separate FPPN and wiggling errors, allowing for their individual calibration and correction, which can be implemented in software using a processor to generate filtered images and correct subsequent ToF images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods are used, then the calibration process is simpler, but FPPN and wiggling errors are combined and cannot be separated, reducing measurement precision
Solution Approach 1:
The calibration process segments the combined FPPN and wiggling errors into separate components. By applying a lowpass filter to the ToF image, the method separates the high-frequency FPPN errors from the low-frequency wiggling errors, allowing independent calibration of each error type and improving overall measurement precision
Solution Approach 2:
The lowpass filter acts as an intermediary tool in the calibration process. It processes the raw ToF image to separate error components frequency-wise, enabling the extraction of FPPN errors without requiring complex hardware modifications or multi-step calibration procedures
2Measurement precision
If lowpass-filtering is applied to separate FPPN and wiggling, then measurement precision improves, but processing time increases
Solution Approach 1:
The method replaces complex mechanical or hardware-based calibration systems with a software-based digital lowpass filter. This substitution maintains high error separation accuracy while significantly reducing calibration time and simplifying the overall calibration process
3Reliability
If multiple ToF images are averaged, then thermal noise is reduced, but acquisition time increases
Solution Approach 1:
The method uses periodic acquisition of multiple ToF images at predetermined distances, followed by averaging to reduce thermal noise. This periodic sampling approach improves image reliability while managing acquisition time through efficient signal averaging
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the quality of ToF images by effectively reducing systematic errors, enhancing the accuracy of distance measurements and reducing thermal noise through averaging multiple images and using specific modulation frequencies.
Implementation Method 1
The at least one ToF image is lowpass-filtered to generate a filtered ToF image
Implementation Method 2
The filtered ToF image is subtracted from the ToF image to generate an FPPN image
Implementation Method 3
Each pixel of the image sensor can measure the time the light has taken to travel from the illumination unit to the object and back to the focal plane array
Implementation Method 4
For RF-modulated light sources, such as LEDs or laser diodes, the light can be modulated with high speeds up to and above 100 MHz
Data Source
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Figure 3a~3f
AI summary
Embodiments of the present disclosure relate to a concept for calibrating a Time of Flight, ToF, camera (20). At least one ToF image of a target (24) located at one or more predetermined distances from the ToF camera is taken (12). The at least one ToF image is lowpass-filtered (14) to generate a filtered ToF image. The filtered ToF image is subtracted (16) from the ToF image to generate a fixed pattern phase noise, FPPN, image