通过融合运动信息提升稻田冠层地图精度，助力农机自主导航。
Motion-Coupled Mapping Algorithm for Hybrid Rice Canopy
- 结合实时RGB-D数据与运动惯性测量，生成带概率分布的高程图。
- 在真实稻田中测试，显著提升农机对冠层高度和结构的估计精度。
- 适合需要精准避障与路径规划的农业无人车应用。
本文提出一种面向杂交水稻冠层轮廓映射的运动耦合算法，专为在复杂未知稻田中导航的农业无人地面车辆（Agri-UGV）设计。精确的冠层映射对Agri-UGV规划高效路径、避开保护区域至关重要。执行除杂等作业的Agri-UGV，其运动控制高度依赖对水稻冠层高度与结构的准确估计。为此，该算法融合实时RGB-D传感器数据与运动学及惯性测量，实现高效映射与本体感知定位。生成基于栅格的高程图，反映冠层轮廓的概率分布，并考虑运动带来的不确定性。算法部署于高离地间隙的Agri-UGV平台，在多种环境（包括受控与动态稻田场景）中进行测试。该方法显著提升了Agri-UGV的映射精度与运行可靠性，推动了更高效的自主农业作业。
原文摘要 · Abstract (English)
This paper presents a motion-coupled mapping algorithm for contour mapping of hybrid rice canopies, specifically designed for Agricultural Unmanned Ground Vehicles (Agri-UGV) navigating complex and unknown rice fields. Precise canopy mapping is essential for Agri-UGVs to plan efficient routes and avoid protected zones. The motion control of Agri-UGVs, tasked with impurity removal and other operations, depends heavily on accurate estimation of rice canopy height and structure. To achieve this, the proposed algorithm integrates real-time RGB-D sensor data with kinematic and inertial measurements, enabling efficient mapping and proprioceptive localization. The algorithm produces grid-based elevation maps that reflect the probabilistic distribution of canopy contours, accounting for motion-induced uncertainties. It is implemented on a high-clearance Agri-UGV platform and tested in various environments, including both controlled and dynamic rice field settings. This approach significantly enhances the mapping accuracy and operational reliability of Agri-UGVs, contributing to more efficient autonomous agricultural operations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。