基于改进粒子群的正交轴系三维激光扫描设备的几何参数标定研究

本文针对研发的正交轴系三维激光扫描设备几何参数标定问题，提出一种基于改进粒子群的标定算法，以解决设备装配几何误差带来的测量精度降低问题。首先，基于三维扫描设备的改进DH参数模型构建待优化几何误差参数并利用粒子群算法在约束范围内进行迭代优化以确定标定值；然后，在传统粒子群算法基础上进行了动态参数(惯性权重、学习因子的动态调整和基于整体最小二乘算法动态平面拟合的适应度函数改进)的优化以解决算法陷入局部最优的问题；最后，采用3种不同方法进行基于标准棋盘标定板的标定实验。实验结果对比分析表明：本文提出的动态平面拟合-参数改进PSO算法大幅提高了标定算法的收敛速度以及适应度计算可靠性，可以对设备进行快速标定，采用该方法标定后的设备其测量的精度也大幅提高。该标定方法为其它正交轴系的测量设备的几何参数标定提供了一种参考。

Research on geometric parameter calibration of 3D-laser-scanner with orthogonal shafting based on improved particle swarm optimization

Aiming at the solving the problem of measurement error from the geometric error calibration in equipment assembly. A calibration algorithm based on improved particle swarm is proposed for the calibration of geometric parameters of 3D laser scanner with orthogonal axes. First, the geometric error parameters to be optimized are constructed based on the improved DH parameter model of the 3D scanning equipment, and the particle swarm algorithm is used to perform iterative optimization within the constraints to determine the calibration value. Then, on the basis of traditional particle swarm optimization, the optimization of dynamic parameters (inertia weight, dynamic adjustment of learning factor and fitness function improvement based on dynamic plane fitting of global least squares algorithm) is carried out to solve the problem that the algorithm falls into local optimum. At last, three different methods are used to carry out the calibration experiments based on the standard checkerboard calibration board. The comparative analysis of the experimental results shows that the dynamic plane fitting-parameter improvement PSO algorithm proposed in this paper greatly improves the convergence speed of the calibration algorithm and the reliability of the fitness calculation, and can quickly calibrate the equipment. The accuracy is also greatly improved. This calibration method provides a reference for the calibration of geometric parameters of other orthogonal axis systems.