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Vision measurement error compensation research of double-theodolite based on Neural Network approaching

机译：基于神经网络接近的双经纬仪视觉测量误差补偿研究

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Three-dimensional spatial coordinate measurement system including two optical theodolites is a kind of common extrinsic calibration equipment. During the course of geometrical dimensional measurement, there will be system error of vertical observing angle's reading in theodolite's certain position, the results of which leads to bad effects of precision. Grounded on actual optical theodolites measurement system, objected to two dimension coordinate simulator, this paper has built the mathematical model of coordinate dot to be measured and observing angle of theodolite taking system error as nonlinear component. The Neural Network (NN) to simulate the nonlinear relationship of observing angles and distances has been established taking the data satisfied by the conditions of error existence for training samples. The idea is provided of acquiring nonlinear mapping relations by NN approaching to compensate system error. In the experiment, the weights of NN have been continuously optimized to get the optimal parameter configuration, and consistence degree of the compensated results and theoretical results has validated this methods.
机译：包括两种光学挖掘机的三维空间坐标测量系统是一种常见的外在校准设备。在几何维度测量过程中，将有垂直观察角度读取的垂直观察角度在经纬仪的某些位置的过程中，结果导致精度的效果不佳。基于实际光学实际模块化测量系统，反对两个尺寸坐标模拟器，本文建立了坐标点的数学模型，测量和观察经纬仪采取系统误差作为非线性组件的角度。为了模拟观察角度和距离的非线性关系的神经网络（NN）已经建立了训练样本的错误存在条件的数据。提供了通过接近补偿系统错误的NN获取非线性映射关系的想法。在实验中，NN的重量已经连续优化以获得最佳参数配置，并且补偿结果的一致程度和理论结果已经验证了这种方法。

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