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Research on Kinect Calibration and Depth Error Compensation Based on BP Neural Network

机译：基于BP神经网络的Kinect校准和深度误差补偿研究

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In order to improve the depth measurement accuracy of Kinect and reduce the depth error, we applied BP neural network to the calibration of Kinect depth camera in the research. The corner information of the target image is extracted as the training set, and the dual neural network is used to calibrate the data information in different directions according to the imaging rules of pixels. The results of neural network training prediction are used to improve the calibration accuracy further. Then, based on the improvement of calibration accuracy, error analysis, and error compensation are performed for the situation where the depth error in Kinect measurement is significant. An error compensation model is established by using the error symmetry of the Kinect depth measurement, and the depth information is compensated by the optical path difference, which reduces the order of parameters and improves the measurement accuracy. Experimental results show that this method can effectively improve the calibration and depth measurement accuracy of Kinect cameras.
机译：为了提高Kinect的深度测量精度并减少深度误差，我们将BP神经网络应用于研究中的Kinect深度相机的校准。提取目标图像的角信息作为训练集，并且双神经网络用于根据像素的成像规则来校准不同方向的数据信息。神经网络训练预测的结果用于进一步提高校准精度。然后，基于校准精度的提高，对Kinect测量中深度误差显着的情况进行了误差分析和误差补偿。通过使用Kinect深度测量的误差对称来建立误差补偿模型，并且通过光路径来补偿深度信息，这减少了参数的顺序并提高了测量精度。实验结果表明，该方法可以有效提高Kinect摄像机的校准和深度测量精度。

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