视觉导航农用车辆相对位姿测量新方法(英文)

Novel pose measurement for agricultural vehicle guided by machine vision

农田环境中农作物大多呈近似直线的行垄分布特点,农用车辆自主视觉导航时通常利用这些景物特征作为跟踪目标。提出了一种计算车辆相对于跟踪目标位姿的新型方法,首先分析了传统算法中存在的计算量大、忽视图像平面中各像素权重不同等缺陷,而后依据跟踪路径局部线性模型假设,详细地推导了算法过程。基于视觉导航原型车辆的试验结果表明,与人工测量值相比,横向距离和航向角的误差均值都等于零,标准差分别为3cm和0.62deg。

Some agricultural tasks consist of applying chemical fertilizer to crops, but the products are often applied throughout the field in most cases, which cause pollution of water and possible chemical residues. In order to apply the products selectively and reduce the quantity of application, an autonomous vehicle can be used. Generally, this kind of vehicle follows the crop rows autonomously in the field where plants are arranged in rows, so its pose relative to crop row is important for tracking algorithm to work. With the machine vision, a novel method to calculate this pose was demonstrated, which could adapt to the complex characteristics of field environment excellently. First, some shortcomings involved in the conventional measuring method were analyzed carefully, such as processing time being long, pixel weight in the digital image being ignored and so on. With the local linear model of the tracked crop row then the algorithm was deduced at full length. Finally, based on the prototype of autonomous agricultural vehicle, the experiment was carried out, and it was shown that compared with the manual measurement the standard deviation of offset was 3 cm and of heading angle 0. 62 deg while without any fixed displacement.