HortiMulti: A Multi-Sensor Dataset for Localisation and Mapping in Horticultural Polytunnels
2026-03-20 • Robotics
Robotics
AI summaryⓘ
The authors present HortiMulti, a new dataset designed for robots operating in strawberry and raspberry greenhouses. It includes data from various sensors collected across different seasons, showing challenges like changing plant appearances and unreliable GPS. The dataset has detailed ground truth information to help test and improve robot navigation and perception. Their tests show that current methods still struggle in these complex environments, making HortiMulti a valuable tool for future research in agricultural robotics.
Agricultural roboticsMultimodal datasetPolytunnel3D LiDARRGB camerasIMUGNSSVisual SLAMLiDAR-inertial odometryFiducial markers
Authors
Shuoyuan Xu, Zhipeng Zhong, Tiago Barros, Matthew Coombes, Cristiano Premebida, Hao Wu, Cunjia Liu
Abstract
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.