基于粒子滤波的苹果采摘机器人目标姿态估算

针对苹果采摘机器人中果实姿态信息缺失造成果实损伤及采摘失败等问题，根据苹果尾部花萼遗迹区域是否可见分析了果实姿态信息机器视觉测量方法，研究了果实姿态信息的粒子滤波估计。将单目摄像机固定在机器人末端手爪上，在手爪趋近果实的采摘过程中，随机采集多帧果实图像。对每帧图像运用惯性主轴或重心点偏移两种方法计算果实在机器人坐标系下的姿态向量值，并通过粒子滤波融合来得到果实姿态信息的最优估计。实验结果表明，粒子滤波方法可以有效地融合多帧图像的苹果姿态信息，能够减小因依靠单帧图像直接测量而产生的误差。

Apple Attitude Estimation Based on Particle Filter for Harvesting Robot

In view of harvesting damages and failures caused by the absence of attitude information in the current apple harvesting robot, the method of measuring apple attitude was put forward with the machine vision, and the optimal estimation of apple attitude was carried out based on the particle filter in real time. A camera was fixed on the robotic gripper, in the process of picking the camera vision field was controlled to cover the expected fruit target, and a series of apple images were sampled randomly. The algorithms of the inertial axis and the center offset were applied to calculate the apple attitude at the robotic coordinate according to the each apple image. Finally, the attitude data extracted from many apple images was fused with the particle filter, and the optimal estimation of the apple attitude was obtained. The experimental results showed that the multiple information of apple attitude was fused successfully, and the measurement error of apple attitude was decreased effectively.