首个面向复杂农田的多传感器无人机数据集，助力农业无人机精准定位
AgriLiRa4D: A Multi-Sensor UAV Dataset for Robust SLAM in Challenging Agricultural Fields
- 构建涵盖平地、丘陵、梯田的六类飞行场景，融合激光雷达、4D雷达与惯导
- 提供高精度FINS_RTK真值轨迹，支持多传感器在低纹理、重复结构下的定位测试
- 适合研究农业无人机鲁棒导航的学者，尤其关注多模态融合与实际田间挑战
多传感器同时定位与地图构建（SLAM）对执行喷洒、巡查等任务的无人机至关重要，但真实多模态农业无人机数据集仍十分稀缺。为此，我们提出AgriLiRa4D，一个面向复杂户外农田环境的多模态无人机数据集。该数据集涵盖平地、丘陵、梯田三种典型农田类型，并包含边界与覆盖两种作业模式，共形成六组飞行序列。数据集提供基于光纤惯性导航系统与实时动态定位技术（FINS_RTK）的高精度真值轨迹，同步采集3D激光雷达、4D雷达和惯性测量单元（IMU）数据，并附完整内参与外参标定信息。依托其全面的传感器配置与多样化的现实场景，AgriLiRa4D可支撑多种SLAM与定位研究，实现对低纹理作物、重复模式、动态植被等农业环境挑战的严格鲁棒性评估。为验证其价值，我们在不同传感器组合下基准测试了四种先进多传感器SLAM算法，凸显所提序列的难度及多模态方法对可靠无人机定位的必要性。该数据集填补了农业SLAM领域关键空白，为研究社区提供重要基准，推动农业无人机自主导航技术发展。数据下载地址：https://zhan994.github.io/AgriLiRa4D。
原文摘要 · Abstract (English)
Multi-sensor Simultaneous Localization and Mapping (SLAM) is essential for Unmanned Aerial Vehicles (UAVs) performing agricultural tasks such as spraying, surveying, and inspection. However, real-world, multi-modal agricultural UAV datasets that enable research on robust operation remain scarce. To address this gap, we present AgriLiRa4D, a multi-modal UAV dataset designed for challenging outdoor agricultural environments. AgriLiRa4D spans three representative farmland types-flat, hilly, and terraced-and includes both boundary and coverage operation modes, resulting in six flight sequence groups. The dataset provides high-accuracy ground-truth trajectories from a Fiber Optic Inertial Navigation System with Real-Time Kinematic capability (FINS_RTK), along with synchronized measurements from a 3D LiDAR, a 4D Radar, and an Inertial Measurement Unit (IMU), accompanied by complete intrinsic and extrinsic calibrations. Leveraging its comprehensive sensor suite and diverse real-world scenarios, AgriLiRa4D supports diverse SLAM and localization studies and enables rigorous robustness evaluation against low-texture crops, repetitive patterns, dynamic vegetation, and other challenges of real agricultural environments. To further demonstrate its utility, we benchmark four state-of-the-art multi-sensor SLAM algorithms across different sensor combinations, highlighting the difficulty of the proposed sequences and the necessity of multi-modal approaches for reliable UAV localization. By filling a critical gap in agricultural SLAM datasets, AgriLiRa4D provides a valuable benchmark for the research community and contributes to advancing autonomous navigation technologies for agricultural UAVs. The dataset can be downloaded from: https://zhan994.github.io/AgriLiRa4D.
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