AgriLiRa4D: A Multi-Sensor UAV Dataset for Robust SLAM in Challenging Agricultural Fields
Zhihao Zhan, Yuhang Ming, Shaobin Li, Jie Yuan

TL;DR
AgriLiRa4D is a comprehensive multi-sensor UAV dataset designed for robust SLAM in diverse challenging agricultural environments, facilitating research and benchmarking of localization algorithms under real-world conditions.
Contribution
This paper introduces AgriLiRa4D, the first multi-modal UAV dataset for agricultural SLAM, including high-accuracy ground-truth and diverse challenging scenarios for robust localization research.
Findings
Multi-sensor SLAM algorithms struggle in low-texture and dynamic environments.
Multi-modal sensor fusion improves UAV localization robustness.
Benchmark results highlight the need for advanced multi-sensor SLAM methods.
Abstract
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Smart Agriculture and AI · UAV Applications and Optimization
