Agricultural robotics is gaining increasing relevance in both research and real-world deployment. As these systems are expected to operate autonomously in more complex tasks, the availability of representative real-world datasets becomes essential. While domains such as urban and forestry robotics benefit from large and established benchmarks, horticultural environments remain comparatively under-explored despite the economic significance of this sector. To address this gap, we present HortiMulti, a multimodal, cross-season dataset collected in commercial strawberry and raspberry polytunnels across an entire growing season, capturing substantial appearance variation, dynamic foliage, specular reflections from plastic covers, severe perceptual aliasing, and GNSS-unreliable conditions, all of which directly degrade existing localisation and perception algorithms. The sensor suite includes two 3D LiDARs, four RGB cameras, an IMU, GNSS, and wheel odometry. Ground truth trajectories are derived from a combination of Total Station surveying, AprilTag fiducial markers, and LiDAR-inertial odometry, spanning dense, sparse, and marker-free coverage to support evaluation under both controlled and realistic conditions. We release time-synchronised raw measurements, calibration files, reference trajectories, and baseline benchmarks for visual, LiDAR, and multi-sensor SLAM, with results confirming that current state-of-the-art methods remain inadequate for reliable polytunnel deployment, establishing HortiMulti as a one-stop resource for developing and testing robotic perception systems in horticulture environments.

翻译：农业机器人在研究与实际部署中的重要性日益凸显。随着此类系统被期望在更复杂任务中自主运行，具有代表性的真实世界数据集的可获得性变得至关重要。尽管城市与林业机器人领域受益于成熟且大规模的标准基准，但园艺环境由于其经济重要性而相对研究不足。为填补这一空白，我们提出了HortiMulti——一个跨季节的多模态数据集，采集自商业草莓和覆盆子多隧道农场的完整生长季，捕获了显著的物体外观变化、动态叶片、塑料覆盖物引起的镜面反射、严重的感知混淆以及全球导航卫星系统不可靠条件，这些因素均直接导致现有定位与感知算法性能下降。传感器套件包含两个三维激光雷达、四个RGB相机、惯性测量单元、全球导航卫星系统及轮式里程计。真值轨迹由全站仪测量、AprilTag基准标记与激光雷达-惯性里程计联合生成，覆盖密集、稀疏及无标记区域，以支持受控与真实条件下的评估。我们发布了时间同步的原始测量数据、标定文件、参考轨迹，以及面向视觉、激光雷达及多传感器同步定位与建图的基线基准，结果证实当前最先进方法仍无法满足可靠的多隧道部署需求，从而确立HortiMulti为园艺环境下机器人感知系统研发与测试的一站式资源。