...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers and Electronics in Agriculture >Three-dimensional perception of orchard banana central stock enhanced by adaptive multi-vision technology
【24h】

Three-dimensional perception of orchard banana central stock enhanced by adaptive multi-vision technology

机译：自适应多视力技术提升了果园香蕉中央山畜的三维感知

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic vision-based picking in orchards and fields is a highly challenging task. The orchard banana central stock, which is large in size, low in color contrast, and falls within a complex background, was taken as the subject in this research. A measurement framework based on multi-vision technology was established, and a set of general methods were utilized to improve the comprehensive performance of multi-view-geometry-based vision modules in orchard picking tasks. Multiple cameras at different angles were deployed to maximize the perception range. The global geometric parameters of the cameras were calibrated and a robust semantic segmentation network was trained to achieve effective image pre-processing. A novel adaptive stereo matching strategy was designed to ensure that the robot reliably completes 3D triangulation at various depths as it moves across the target area. Global calibration errors were corrected via a high-accuracy point cloud stitching algorithm. Experimental results indicated that the proposed adaptive stereo matching strategy was accurate to different sampling depths and showed stable performance, and the proposed point cloud stitching algorithm accurately stitched multi-view point clouds. This work provides theoretical and practical references for the 3D sensing of banana central stocks in complex environments. The proposed technique was designed for adaptability of the multi-vision system for field perception, so it can be easily transferred to similar applications such as the 3D reconstruction of agricultural targets, 3D positioning of fruit clusters, and 3D robotic arm obstacle avoidance.
机译：果园和领域的自动视觉拣选是一个高度挑战的任务。果园香蕉中央库存大小，颜色对比度低，并在复杂的背景中落下，被视为本研究中的主题。建立了一种基于多视觉技术的测量框架，并利用了一系列一般方法来提高果园采摘任务中的基于多视图 - 几何视觉模块的综合性能。部署不同角度的多个相机以最大化感知范围。校准相机的全局几何参数，培训鲁棒语义分割网络以实现有效的图像预处理。设计了一种新颖的自适应立体声匹配策略，以确保机器人可靠地完成在各种深度的3D三角测量，因为它在目标区域上移动。通过高精度点云拼接算法校正了全局校准误差。实验结果表明，所提出的自适应立体声匹配策略对不同的采样深度准确并显示出稳定的性能，所提出的点云拼接算法精确缝合多视点云。这项工作为复杂环境中的香蕉中央股3D感测提供了理论和实践参考。所提出的技术被设计用于对现场感知的多视觉系统的适应性，因此可以很容易地转移到类似的应用，例如农业目标的3D重建，果实集群的3D定位，以及3D机器人手臂障碍避免。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号