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.

翻译：多传感器同步定位与建图（SLAM）对于执行喷洒、勘测与巡检等农业任务的无人机至关重要。然而，能够支撑鲁棒运行研究的真实世界多模态农业无人机数据集仍十分匮乏。为填补这一空白，我们提出了AgriLiRa4D——一个专为复杂户外农业环境设计的无人机多模态数据集。AgriLiRa4D涵盖平坦、丘陵与梯田三种典型农田类型，包含边界巡航与覆盖作业两种飞行模式，共形成六组飞行序列。该数据集提供了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，以及同步采集的三维激光雷达、四维毫米波雷达与惯性测量单元（IMU）数据，并附有完整的传感器内参与外参标定文件。凭借其全面的传感器配置与多样化的真实场景，AgriLiRa4D可支持多种SLAM与定位研究，并能针对低纹理作物、重复模式、动态植被等真实农业环境中的挑战进行严格的鲁棒性评估。为进一步展示其应用价值，我们在不同传感器组合下对四种先进的多传感器SLAM算法进行了基准测试，结果凸显了所提序列的难度以及采用多模态方法实现无人机可靠定位的必要性。通过填补农业SLAM数据集的关键空白，AgriLiRa4D为研究社区提供了有价值的基准平台，并有助于推动农业无人机自主导航技术的发展。数据集可通过以下链接下载：https://zhan994.github.io/AgriLiRa4D。