Purpose

Accurately, positioning is a fundamental requirement for vision measurement systems. The calculation of the harvesting width can not only help farmers adjust the direction of the intelligent harvesting robot in time but also provide data support for future unmanned vehicles.

Design/methodology/approach

To make the length of each pixel equal, the image is restored to the aerial view in the world coordinate system. To solve the problem of too much calculation caused by too many particles, a certain number of particles are scattered near the crop boundary and the distribution regularities of particles’ weight are analyzed. Based on the analysis, a novel boundary positioning method is presented. In the meantime, to improve the robustness of the algorithm, the back-projection algorithm is also used for boundary positioning.

Findings

Experiments demonstrate that the proposed method could well meet the precision and real-time requirements with the measurement error within 55 mm.

Originality/value

In visual target tracking, using particle filtering, a rectangular is used to track the target and cannot obtain the boundary information. This paper studied the distribution of the particle set near the crop boundary and proposed an improved particle filtering algorithm. In the algorithm, a small amount of particles is used to determine the crop boundary and accurate positioning of the crop boundary is realized.

中文翻译：

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使用视觉定位未收获区域的作物边界的改进粒子过滤

目的

准确地说，定位是视觉测量系统的基本要求。收获宽度的计算不仅可以帮助农民及时调整智能收获机器人的方向，还可以为未来的无人车提供数据支持。

设计/方法/方法

为了使每个像素的长度相等，将图像恢复到世界坐标系中的鸟瞰图。为解决粒子过多导致计算量过大的问题，在作物边界附近散布一定数量的粒子，分析粒子权重的分布规律。在此分析的基础上，提出了一种新的边界定位方法。同时，为了提高算法的鲁棒性，还采用了反投影算法进行边界定位。

发现

实验表明，该方法能够很好地满足精度和实时性要求，测量误差在55 mm以内。

原创性/价值

在视觉目标跟踪中，使用粒子滤波，使用矩形跟踪目标，无法获取边界信息。本文研究了作物边界附近粒子集的分布，提出了一种改进的粒子滤波算法。该算法利用少量粒子来确定裁剪边界，实现裁剪边界的精确定位。