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：农业机器人正日益在科研与实地部署中受到重视。随着这些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变得至关重要。尽管城市与林业机器人等领域受益于庞大且成熟的基准数据集，园艺环境在此领域仍然相对缺乏探索，尽管该行业具有显著经济价值。为填补这一空白，我们提出HortiMulti，一个在商业草莓和覆盆子隧道中、覆盖整个生长季采集的多模态跨季节数据集，其捕获了显著的外观变化、动态叶片、塑料覆盖物的镜面反射、严重的感知混淆以及GNSS不可靠的条件，这些因素均直接降低现有定位与感知算法的性能。传感器套件包括两个3D激光雷达、四个RGB相机、一个惯性测量单元、GNSS以及轮式里程计。真实轨迹由全站仪测量、AprilTag基准标记和激光雷达-惯性里程计组合生成，覆盖密集、稀疏及无标记场景，以支持在受控和实际条件下的评估。我们发布时间同步原始测量数据、标定文件、参考轨迹以及视觉、激光雷达和多传感器同步定位与建图的基准测试结果，证实当前最先进方法仍难以在隧道环境中可靠部署，从而将HortiMulti确立为在园艺环境中开发与测试机器人感知系统的一站式资源。