平行双目视觉系统的定位误差补偿

测量精度在平行双目视觉系统的应用中非常重要.为了提高测量系统的精度,提出了基于改进的BP神经网络的误差补偿策略。采用不同位置处的测量数据作为学习样本,利用训练好的网络模型预测测量系统的误差,对测量结果进行误差补偿,得到新的数据作为测量值。实验结果表明,该方法的结果值相较原始数据,误差减少了70%,为提高视觉系统的定位精度提供了一种新的思路。

Positioning Error Compensation for Parallel Binocular Vision Systems

Measurement accuracy is very important in the application of parallel binocular vision systems. In order to improve the accuracy of the measurement system,a measurement error compensation strategy based on an improved BP neural network was proposed. The measurement data at different locations are used as learning samples. The trained network model is used to predict the error of the measurement system. Errors are compensated for the measurement results and new data are obtained as the measurement values. The experimental results show that compared with the original data,the error of this method is reduced by 70%,which provides a new idea for improving the positioning accuracy of the visual system.