AgriLiRa4D：一种多传感器无人机数据集，用于在具有挑战性的农业田间实现鲁棒SLAM
AgriLiRa4D: A Multi-Sensor UAV Dataset for Robust SLAM in Challenging Agricultural Fields
AI总结：
AgriLiRa4D提供多传感器数据集，用于在农业环境中实现鲁棒SLAM，支持多种SLAM研究和评估
摘要：
多传感器同时定位与建图（SLAM）对于执行农业任务（如喷洒、勘测和检查）的无人驾驶飞行器（UAV）至关重要。然而，现实世界中能够支持研究鲁棒操作的多模态农业UAV数据集仍然稀缺。为了解决这一差距，我们提出了AgriLiRa4D，一种为具有挑战性的户外农业环境设计的多模态UAV数据集。AgriLiRa4D涵盖了三种代表性的农田类型——平坦、丘陵和梯田，并包含边界和覆盖操作模式，从而产生六个飞行序列组。该数据集提供了来自光纤惯性导航系统（FINS_RTK）的高精度地面真实轨迹，以及来自3D激光雷达、4D雷达和惯性测量单元（IMU）的同步测量，并附有完整的内在和外在校准。利用其全面的传感器套件和多样的现实世界场景，AgriLiRa4D支持多种SLAM和定位研究，并能够对低纹理作物、重复模式、动态植被等现实农业环境中的挑战进行严格评估。为进一步展示其用途，我们对四种最先进的多传感器SLAM算法在不同传感器组合上进行基准测试，突显了所提出序列的难度以及多模态方法在可靠UAV定位中的必要性。通过填补农业SLAM数据集中的关键空白，AgriLiRa4D为研究社区提供了一个有价值的基准，并有助于推动农业UAV的自主导航技术发展。该数据集可从：https://zhan994.github.io/AgriLiRa4D下载。
英文摘要：
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.
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。
