用低成本机器人精准定位草莓花位置，助力自动化授粉。
Robotic 3D Flower Pose Estimation for Small-Scale Urban Farms
- 将点云沿三轴投影成六视角图像，结合2D检测提取花点云。
- 识别率约80%，花姿误差均值7.7度，满足机器人授粉需求。
- 适合做智能农业、机器人植保的开发者参考。
小规模城市农场与低成本机器人（如FarmBot）的结合为植物表型分析提供了可及平台。本文使用配备自定义摄像头末端执行器的FarmBot，从获取的3D点云模型中估计草莓植株花朵姿态（用于机器人授粉）。提出一种新算法：将点云沿正交轴方向转换为六个视角的二维图像，对每幅图像使用2D目标检测模型识别花的二维边界框，并将其映射回三维空间以提取花点云。通过拟合三种几何形状（超椭球体、抛物面和平面）进行姿态估计，并与人工标注的真值对比。该方法在定制化的FarmBot平台上成功检测到约80%的花朵，平均姿态误差为7.7度，已达到机器人授粉的实用水平，且性能媲美先前结果。所有代码将公开于 https://github.com/harshmuriki/flowerPose.git。
原文摘要 · Abstract (English)
The small scale of urban farms and the commercial availability of low-cost robots (such as the FarmBot) that automate simple tending tasks enable an accessible platform for plant phenotyping. We have used a FarmBot with a custom camera end-effector to estimate strawberry plant flower pose (for robotic pollination) from acquired 3D point cloud models. We describe a novel algorithm that translates individual occupancy grids along orthogonal axes of a point cloud to obtain 2D images corresponding to the six viewpoints. For each image, 2D object detection models for flowers are used to identify 2D bounding boxes which can be converted into the 3D space to extract flower point clouds. Pose estimation is performed by fitting three shapes (superellipsoids, paraboloids and planes) to the flower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. All code will be made available at https://github.com/harshmuriki/flowerPose.git.
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