HortiMulti: A Multi-Sensor Dataset for Localisation and Mapping in Horticultural Polytunnels

Shuoyuan Xu; Zhipeng Zhong; Tiago Barros; Matthew Coombes; Cristiano Premebida; Hao Wu; Cunjia Liu

cs.ROMar 20, 2026

HortiMulti: A Multi-Sensor Dataset for Localisation and Mapping in Horticultural Polytunnels

Authors: Shuoyuan Xu, Zhipeng Zhong, Tiago Barros, Matthew Coombes, Cristiano Premebida, Hao Wu, Cunjia Liu

Organizations: Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire, UK, LE11 3TU · Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal · Antobot Ltd, Arise Innovation Hub, Chelmsford, Essex, UK, CM1 1SQ

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.

Figures & tables

Figure 1: HortiMulti is a multi-seasonal dataset collected in strawberry (left) and raspberry (right) polytunnels. Data are synchronised across multiple LiDARs and multiple cameras; additional sensors include an AHRS, GNSS, and wheel odometry (bottom). Ground truth is obtained using AprilTags and surveying equipment (bottom right).

Dataset	Platform	Lighting	Season	Sensor					Ground Truth
				LiDAR	Mono	Stereo	IMU	Wheel Odom
Forest and Off-Road
TB-Places [ 16 ]	UGV	x	x	x	✓	x	✓	✓	Laser tracker
Wild-Places [ 17 ]	Handheld	x	✓	✓	✓	x	✓	x	SLAM
FinnForest [ 18 ]	Car	✓	✓	x	✓	✓	✓	x	GNSS/INS
SFU Mountain [ 19 ]	UGV	✓	x	✓	✓	✓	✓	✓	Manually-aligned locations

Table 1: Summry of agricultural / off-road SLAM datasets.

Figure 2: Robot platform and sensor tower

Symbol	Dim. (mm)	Symbol	Dim. (mm)	Symbol	Dim. (mm)
A	48	G	121	M	303
B	51	H	199	N	160
C	210	I	43	O	113
D	573	J	197	P	108
E	330	K	505	Q	63
F	340	L	305	R	76

Table 2: Geometric parameters of the mobile sensing platform (labels refer to Fig. 2 )

Sensor	Model	Specification
Spinning LiDAR	Velodyne VLP-16	16 channels, 360 ∘ $\times$ 40 ∘ FOV, 200 m range, 10 Hz, $\pm 3$ cm accuracy
Spinning LiDAR	Ouster OS0-64	64 channels, 360 ∘ $\times$ 90 ∘ FOV, 100 m range, 10 Hz, $\pm 0.8$ cm accuracy
IMU	MicroStrain 3DM-GX5-AHRS	3-axis gyro, accel, mag, 200 Hz, $\pm 0.05^{\circ}$ roll/pitch, $\pm 0.1^{\circ}$ yaw
RGB Camera	Basler ace U acA2040-55uc	3.2 MP, 1936 $\times$ 1216 px, 10 fps global shutter
GNSS	u-blox F9P GPS	Multi-band GNSS, RTK $<$ 0.02 m (RTK fix), 8 Hz output
Wheel Odometry	Encoders	Differential drive, resolution $<$ 1 mm per tick

Table 3: Sensor Specifications

Calibration	Precision	Tool
Camera to Camera	$\leq$ 0.33 px reprojection error	[ 36 ]
Camera to IMU	0.52 px reprojection error	[ 36 , 37 ]
Camera to LiDAR	0.943 px reprojection error	[ 38 ]
LiDAR to LiDAR	fitness: 0.999	[ 39 ]

Table 4: Calibration quality summary

Figure 3: Example of a colourised point cloud with corresponding image (bottom left).

Figure 4: Example of some representative trajectories, designed to mimic real farm machinery operations: cross-tunnel passes that mimic spraying tasks, and row-wise traverses within a single polytunnel that resemble UV treatment and potential harvesting robots.

Sensor	Topic Name	ROS Message Type	Rate
Ouster LiDAR	/ouster/points	sensor_msgs/PointCloud2	$\sim$ 10 Hz
Velodyne LiDAR	/velodyne_points	sensor_msgs/PointCloud2	$\sim$ 10 Hz
Forward-Left Camera	/forwardLeft/image_raw/compressed	sensor_msgs/CompressedImage	$\sim$ 10 Hz
	/forwardLeft/camera_info	sensor_msgs/CameraInfo	$\sim$ 10 Hz
	/forwardLeft/tag_detections	apriltag_ros/AprilTagDetectionArray	$\sim$ 10 Hz
Forward-Right Camera	/forwardRight/image_raw/compressed	sensor_msgs/CompressedImage	$\sim$ 10 Hz

Table 5: Topics stored in each data session.

Panel of Figure 6 — Figure 6: Ground truth data collection: (a) and (b) illustrate AprilTag installations in strawberry and raspberry polytunnels, respectively; (c)–(f) show the total station and prism setup used to establish a control network and survey the AprilTag positions; (g)–(i) present representative survey results, with (g) showing all tags within a polytunnel, (h) showing all tags without polytunnel obstruction, and (i) indicating how many tags in (h) were surveyed.

Sequence	Frames	Row Count	Query Pos.	Valid Loops	Loop Ratio (%)	Min Dist (m)	Mean Dist (m)	Median Dist (m)	Max Dist (m)	Std Dist (m)
Nov2025/Strawberry_1	932	4	437	437	47	0.008	0.220	0.214	0.687	0.110
Jun2025/Strawberry_1	1953	7	723	723	37	0.007	0.936	0.244	9.850	2.096
Jun2025/Raspberry_1	340	4	181	181	53	0.017	0.225	0.168	1.929	0.267
Aug2025/Raspberry_1	697	8	394	394	57	0.013	1.074	0.349	7.898	1.827

Table 6: Ground truth statistics for place recognition. Configuration used for all sequences: warm-up frames (skip first N) = 100, lower-bound frame gap (ignore immediate past) = 50, distance threshold = 10 m, and top-1 closest neighbour.

Configuration	RMSE (m)	ME (m)	Max error (m)
Poly-TagSLAM	0.1414	0.1269	0.2937
Poly-TagSLAM + Total Station	0.0726	0.0351	0.2633
LOO validation (held-out)	0.0655	0.0586	0.1403

Table 7: Landmark Mapping Accuracy

Figure 7: Cumulative distribution of localisation errors for held-out landmarks under LOO cross-validation.

Panel of Figure 8 — Figure 8: Qualitative trajectory comparisons within the polytunnel. (a, b) sequence Feb2026 $/$ Strawberry $\_1$ , (c, d) sequence Feb2026 $/$ Strawberry $\_2$ (e, f) sequence Feb2026 $/$ Strawberry $\_3$ , and (g, h) sequence Feb2026 $/$ Strawberry $\_4$ . These figures illustrate the performance and stability of SLAM algorithms with different designed robot trajectories, showing variations in drift, trajectory consistency, and overall accuracy

Stat/Sequence (Feb2026/Strawberry_)	1		2		3		4
	RPE (%)	ATE (m)	RPE (%)	ATE (m)	RPE (%)	ATE (m)	RPE (%)	ATE (m)
Duration (s)	876		952		242		348
Distance (m)	951.60		940.90		231.02		350.37
Size (GB)	37.1		40.3		10.2		16.9
DSO (Mono)	$\times$	$\times$	32.07	10.2568	43.53	14.1276	22.84	2.2490
ORB3 (Mono)	$\times$	$\times$	$\times$	$\times$	19.42	0.4920	11.16	1.5589

Table 8: Sequences specifications and SOTA SLAM performance comparison

Panel of Figure 9 — Figure 9: Retrieval performance for the top-25 candidates within a 10 m range

	Jun2025/Strawberry_1			Nov2025/Strawberry_1			Jun2025/Raspberry_1			Aug2025/Raspberry_1
	@5m	@10m	@25m	@5m	@10m	@25m	@5m	@10m	@25m	@5m	@10m	@25m
SPVSoAP3D [ 51 ]	0.201	0.245	0.356	0.251	0.306	0.384	0.633	0.689	0.768	0.309	0.431	0.519
PointNetVLAD [ 52 ]	0.452	0.508	0.598	0.531	0.591	0.660	0.836	0.876	0.944	0.212	0.366	0.428
PointNetPGAP [ 53 ]	0.504	0.576	0.668	0.508	0.637	0.692	0.797	0.842	0.915	0.430	0.558	0.596
LoGG3D-Net [ 54 ]	0.068	0.094	0.148	0.083	0.110	0.182	0.282	0.356	0.554	0.091	0.177	0.265
OverlapTransformer [ 55 ]	0.028	0.045	0.076	0.094	0.136	0.209	0.254	0.322	0.395	0.064	0.171	0.227

Table 9: Retrieval performance, using recall, of the top-1 retrieved candidate for various threshold range.

Figure 10: Illustration of the top-1 true positive predictions within a 10 m range. For clear visualization only a fraction of the true positives are plotted.

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Jun 9, 2026J/K move · Enter open · S save

From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting

Woojung Son, Won Suk Lee, Zijing Huang, Daeun Choi+3

Department of Agricultural and Biological Engineering, University of Florida · Department of Electrical and Computer Engineering, University of Florida · Edwardson School of Industrial Engineering, Purdue University+1

Sim-to-Real Transfer Object Pose Estimation Synthetic Data Generation for Robotics Robot Manipulation Benchmarks Agricultural Robotics

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Robotic strawberry harvesting requires precise 6D pose estimation; however, collecting 6D pose ground truth in real agricultural fields is inherently challenging. Existing strawberry 6D pose estimation studies have therefore relied mainly on synthetic data, leaving their in-field performance unquantified. In this work, we obtain ground truth indirectly, by recovering camera poses via PnP, reconstructing each scene at metric scale, and annotating a single 3D bounding box per strawberry that is propagated across all frames, yielding, to the best of our knowledge, the first real-world 6D pose ground-truth dataset of red-stage strawberries collected at an actual strawberry farm (12,040 images). We also introduce a synthetic dataset rendered in NVIDIA Isaac Sim, featuring scene-level realism and domain randomization. Despite this improved simulation setup, models trained on synthetic data alone fail to transfer to in-field images, while introducing a small amount of real data improves both translation and rotation accuracy across all backbones. Under the monocular RGB setting evaluated here, rotation is largely recovered once real data is used, and depth is what limits pose accuracy. These baselines across backbone encoders serve as a reference for future work. The real-world dataset is publicly available at https://huggingface.co/datasets/WoojungSon/FieldStraw6D, and the data-collection pipeline is available at https://github.com/wjson2435/FieldStraw6D-pipeline.

Symbol	Dim. (mm)	Symbol	Dim. (mm)	Symbol	Dim. (mm)
A	48	G	121	M	303
B	51	H	199	N	160
C	210	I	43	O	113
D	573	J	197	P	108
E	330	K	505	Q	63
F	340	L	305	R	76

Symbol	Dim. (mm)	Symbol	Dim. (mm)	Symbol	Dim. (mm)
A	48	G	121	M	303
B	51	H	199	N	160
C	210	I	43	O	113
D	573	J	197	P	108
E	330	K	505	Q	63
F	340	L	305	R	76

Abstract

Explore similar work

From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting

VinePT-Map: Pole-Trunk Semantic Mapping for Resilient Autonomous Robotics in Vineyards

GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation

Symbol	Dim. (mm)	Symbol	Dim. (mm)	Symbol	Dim. (mm)
A	48	G	121	M	303
B	51	H	199	N	160
C	210	I	43	O	113
D	573	J	197	P	108
E	330	K	505	Q	63
F	340	L	305	R	76