A rapid calibration and correction method and a TOF camera thereof

Abstract

The invention relates to a rapid calibration and correction method and a TOF camera thereof, and the method comprises the following steps of (1), collecting the related data of the TOF camera at a fixed frequency, including a depth value image, an integral time value, and real distance value data; (2) processing the data obtained in the step (1), and generating related calibration parameters including a global deviation value, a weight lookup table and an FPPN lookup table; and (3) according to the related calibration parameters generated in the step (2), performing distance correction on theTOF camera, including preprocessing, lighting correction and FPPN correction. The method is simple in principle and simple and convenient to operate, can ensure that the TOF camera has higher measurement precision, can meet the speed requirement in the correction process, and is very suitable for engineering application.

Description

technical field [0001] The invention relates to a calibration method and a camera, in particular to a fast calibration and calibration method and a TOF camera. Background technique [0002] With the continuous development of computer vision technology, depth cameras based on TOF (TimeofFlight) technology are more and more widely used. It uses a low-cost CMOS pixel array and active modulation light source technology. The light source emits modulated near-infrared light, which is reflected after encountering an object. The sensor obtains 3D scene depth information by calculating the time difference or phase difference between light emission and reflection. While acquiring depth information, the TOF camera can also acquire grayscale information of space objects. TOF technology has the characteristics of high precision, high frame rate, no dependence on ambient light, and low cost. It is an important milestone in the field of computer vision. [0003] Due to the characteristic...

AI Technical Summary

Problems solved by technology

Due to hardware limitations, the transmitted signal is not a perfectly standard waveform, which produces a continuous offset relative to the measured value

2. Errors caused by different integration times

Especially if the integration time is too large or too small, it will lead to "overexposure" or "underexposure", resulting in a large measurement error

3. Error caused by fixed phase deviation (also known as "FPPN" (FixedPhasePatternNoise))

Although this method has high precision, the steps are cumbersome, and it will cause the variable dimension in the TOF camera correction process to be too high, which will affect the running speed of the correction algorithm.

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