在果园中利用地面移动机器人进行实时树木检测与几何性状估计
On-the-Go Tree Detection and Geometric Traits Estimation with Ground Mobile Robots in Fruit Tree Groves
AI总结：
本文提出融合RGN相机NDVI与3D LiDAR点云的框架，通过熵驱动地标关联和Kalman滤波，使地面机器人实时检测果树并估计宽度、高度。
摘要：
逐树信息采集是精准农业中的一项基本任务，通常由地面移动传感器完成，但可能耗费大量时间和人力。本文提出一种算法框架，用于在田间利用轮式移动机器人实时、在行进中检测树木并估计关键几何特征（即宽度和高度）。该方法基于对二维领域专用数据（通过红-绿-近红外［RGN］相机获取的归一化差异植被指数［NDVI］）与三维LiDAR点云的融合，借助定制的树木地标关联与参数估计算法实现。所提出的系统采用多模态、基于熵的地标对应方法，并集成到底层Kalman滤波器系统中，以识别周围树木并联合估计其空间特征和基于植被的特征。研究使用逼真的仿真测试评估所提算法在多种设置下的表现。农田中的物理实验验证了该方法仅依靠机载计算和传感资源，即可在行进中实时获取准确逐树信息的有效性。
英文摘要：
By-tree information gathering is an essential task in precision agriculture achieved by ground mobile sensors, but it can be time- and labor-intensive. In this paper we present an algorithmic framework to perform real-time and on-the-go detection of trees and key geometric characteristics (namely, width and height) with wheeled mobile robots in the field. Our method is based on the fusion of 2D domain-specific data (normalized difference vegetation index [NDVI] acquired via a red-green-near-infrared [RGN] camera) and 3D LiDAR point clouds, via a customized tree landmark association and parameter estimation algorithm. The proposed system features a multi-modal and entropy-based landmark correspondences approach, integrated into an underlying Kalman filter system to recognize the surrounding trees and jointly estimate their spatial and vegetation-based characteristics. Realistic simulated tests are used to evaluate our proposed algorithm's behavior in a variety of settings. Physical experiments in agricultural fields help validate our method's efficacy in acquiring accurate by-tree information on-the-go and in real-time by employing only onboard computational and sensing resources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
