iToF, indirect Time-of-Flight, camera predicts depth by detecting the phase difference between the light-wave signal and the object, such as Kinect 2.0, and Samsung Galaxy mobile phone. However, iToF camera will easily be affected by the external environment when detecting depth, for example, sunlight temperature, time difference received by each pixel on the receiver, and so on. All need to be calibrated before leaving the factory to ensure depth accuracy. This study will mainly discuss the systematic errors of iToF: Wiggling, Fixed Phase Pattern Noise, Temperature Drift, Lens Distortion: the four main errors that affect results. This thesis will first discuss how to calculate the depth information through different phases detected by iToF sensor, and then use Fourier transform to find the highest and second-highest frequency intervals, then use inverse Fourier transform to push back the correction of the wiggling error for each pixel. Besides, to minimize the fixed phase pattern noise, the actual depth errors are evaluated through the polynomial function fitting method. To get better results, we also focus on temperature drift by look-up table through the statistics of the thermal chamber at different temperatures. Finally, we use pinhole camera model to calibrate lens distortion.