Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting.

Wei Zhang,Wenjie Wang,Chengliang Liu,Liang Gong,Zhonghua Miao,Suyue Chen

doi:10.3390/s21041166

Wei Zhang, Wenjie Wang + Show 4 more

Open Access

https://doi.org/10.3390/s21041166

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Abstract

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

Highlights

IntroductionGood quality silage, which is a major source of roughage, is produced from the green forage of corn, wheat, grass or other crops [5]
The method processes include data preprocessing, pose transformation of the point cloud based on the singular value decomposition (SVD) algorithm, the upper edge segmentation of the container, the upper edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and visualization of identification and positioning results on the depth image
The method processes include data preprocessing, point cloud pose transformation based on the SVD algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the RANSAC

Summary

Introduction

Good quality silage, which is a major source of roughage, is produced from the green forage of corn, wheat, grass or other crops [5]. Compared to these green crops, silage is easier to store and transport, and its nutritional quality is more stable [6]. These advantages, together with low production costs, have greatly increased the demand for silage in modern intensive farms [7]

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