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：农业机器人技术无论是在研究领域还是实际部署中都日益受到重视。随着这些系统被期望在更复杂的任务中自主运行，具有代表性的真实世界数据集的可用性变得至关重要。尽管城市和林业机器人等领域得益于庞大且成熟的基准测试，但园艺环境由于该领域的经济重要性而相对未被充分探索。为填补这一空白，我们提出了HortiMulti，这是一个多模态、跨季节的数据集，采集自商业草莓和覆盆子多隧道大棚，覆盖整个生长季节，捕捉了显著的外观变化、动态叶片、塑料覆盖物的镜面反射、严重的感知模糊以及全球导航卫星系统（GNSS）信号不可靠的条件，所有这些因素都会直接降低现有定位和感知算法的性能。传感器套件包括两个三维激光雷达（LiDAR）、四个RGB相机、一个惯性测量单元（IMU）、GNSS和轮式里程计。真实轨迹来源于全站仪测量、AprilTag基准标记物和激光雷达-惯性里程计的组合，覆盖了密集、稀疏和无标记等不同情况，以支持在受控和真实条件下进行评估。我们发布了时间同步的原始测量数据、标定文件、参考轨迹，以及针对视觉、激光雷达和多传感器同时定位与建图（SLAM）的基线基准测试，结果证实当前最先进的方法仍不足以在多隧道大棚中可靠部署，从而将HortiMulti确立为在园艺环境中开发和测试机器人感知系统的一站式资源。