Unmanned and intelligent agricultural systems are crucial for enhancing agricultural efficiency and for helping mitigate the effect of labor shortage. However, unlike urban environments, agricultural fields impose distinct and unique challenges on autonomous robotic systems, such as the unstructured and dynamic nature of the environment, the rough and uneven terrain, and the resulting non-smooth robot motion. To address these challenges, this work introduces an adaptive LiDAR odometry and mapping framework tailored for autonomous agricultural mobile robots operating in complex agricultural environments. The proposed framework consists of a robust LiDAR odometry algorithm based on dense Generalized-ICP scan matching, and an adaptive mapping module that considers motion stability and point cloud consistency for selective map updates. The key design principle of this framework is to prioritize the incremental consistency of the map by rejecting motion-distorted points and sparse dynamic objects, which in turn leads to high accuracy in odometry estimated from scan matching against the map. The effectiveness of the proposed method is validated via extensive evaluation against state-of-the-art methods on field datasets collected in real-world agricultural environments featuring various planting types, terrain types, and robot motion profiles. Results demonstrate that our method can achieve accurate odometry estimation and mapping results consistently and robustly across diverse agricultural settings, whereas other methods are sensitive to abrupt robot motion and accumulated drift in unstructured environments. Further, the computational efficiency of our method is competitive compared with other methods. The source code of the developed method and the associated field dataset are publicly available at https://github.com/UCR-Robotics/AG-LOAM.

翻译：无人化与智能化农业系统对于提升农业生产效率、缓解劳动力短缺问题至关重要。然而，与城市环境不同，农田环境对自主机器人系统提出了独特且严峻的挑战，例如环境的非结构化与动态特性、崎岖不平的地形以及由此导致的机器人非平稳运动。为应对这些挑战，本研究提出了一种专为在复杂农业环境中运行的自主农业移动机器人设计的自适应激光雷达里程计与建图框架。该框架包含一个基于稠密广义迭代最近点（Generalized-ICP）扫描匹配的鲁棒激光雷达里程计算法，以及一个考虑运动稳定性和点云一致性以实现选择性地图更新的自适应建图模块。该框架的核心设计原则是通过剔除运动畸变点和稀疏动态物体，优先保障地图的增量一致性，这反过来使得基于地图进行扫描匹配所估计的里程计具有更高的精度。通过在真实农业环境中采集的、涵盖多种种植类型、地形类型和机器人运动模式的田间数据集上，与前沿方法进行广泛对比评估，验证了所提方法的有效性。结果表明，我们的方法能够在多样化的农业场景中持续、鲁棒地实现精确的里程计估计与建图结果，而其他方法对非结构化环境中的机器人突变运动和累积漂移较为敏感。此外，我们的方法在计算效率方面与其他方法相比具有竞争力。所开发方法的源代码及相关田间数据集已在 https://github.com/UCR-Robotics/AG-LOAM 公开。