SLAM Dataset 2026

Advancing the field with a common benchmark

Hilti, Trimble, and the Dynamic Robot Systems Group of the University of Oxford have joined forces to launch the SLAM Challenge 2026. The intention is to provide an open and realistic dataset, captured directly on active construction sites using 360° camera with IMU measurements and floor plan priors, to evaluate and compare SLAM systems in real industrial conditions. The challenge offers two categories: SLAM (estimating the camera trajectory in any reference frame) and Localization (estimating the camera trajectory within the floor plan reference frame). The dataset is publicly available, and we encourage everyone to use it for research and development purposes.

Floor name	# Runs	GDrive	Truth
Floor 1	3	Link	Link
Floor 2	5	Link	Link
Floor 3	2	Link	Link
Floor 4	2	Link	Link
Floor 5	1	Link	Link

Floor name	# Runs	GDrive	Truth
Floor 6	4	Link	Link
Floor 7	3	Link	Link
EG	3	Link	Link
UG1	6	Link	Link
UG2	1	Link	Link

Evaluation, file format & more

Submissions are ranked based on trajectory completeness and position accuracy. Each pose is scored using an exponential accuracy model that heavily rewards precise localization. Details about the evaluation scoring, structure of the dataset and helper tools is available on the Github repository.

GitHub

Citation

When using this work in an academic context, please cite in the following manner:

@misc{slamchallenge2026,
    title = {{Hilti}-{Trimble}-{Oxford} Dataset: 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization},
    author = {Centanni, Samuele and Zhang, Yuhao and Tao, Yifu and Kindle, Julien and Neuhaus, Frank and Koß, Tilman and Patel, Aryaman and Helmberger, Michael and Szymańska, Emilia and Gräber, Torben and Fallon, Maurice},
    year = {2026},
    eprint = {2607.06464},
    url = {https://arxiv.org/abs/2607.06464}
}

License

All datasets and benchmarks on this page are copyright by us and published under the Creative Commons Attribution NonCommercial ShareAlike 3.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.

Contact

Any dataset-related questions and concerns can be raised as issues at github.com/Hilti-Research/hilti-trimble-slam-challenge-2026/issues

Other queries - including about the webpage, the evaluation server operation or the conference workshop - should be forwarded to challenge@hilti.com

Sensor suite

360° videos, IMU & floor plan priors

The dataset was captured using the Insta360 One-RS 1-Inch 360 Edition. The camera includes a 6-axis Inertial Measurement Unit (3-axis gyroscope + 3-axis accelerometer). The corresponding motion measurements were recorded onboard and embedded directly into the video metadata.

For validation purposes, a high-accuracy ground-truth trajectory was obtained using LiDAR-Inertial SLAM. During data collection, a LiDAR mapping device equipped with a Hesai XT32 LiDAR sensor was rigidly attached to the camera rig. This sensor was used exclusively to produce the reference trajectory, and its raw measurements are not included in the released dataset.

Hilti Group

Founded in 1941 and headquartered in Schaan, Liechtenstein, Hilti provides leading products, systems, software, and services for the global construction industry. Hilti supports professionals across a wide range of applications, from drilling and fastening to fire protection, on-site productivity, and digital construction workflows. With a strong focus on innovation and engineering excellence, Hilti helps customers build safer, faster, and more sustainably.

Trimble Inc.

Trimble is a global technology company that connects the physical and digital worlds, transforming the ways work gets done. With relentless innovation in precise positioning, modeling, and data analytics, Trimble enables essential industries including construction, geospatial, and transportation. Whether it's helping customers build and maintain infrastructure, design and construct buildings, optimize global supply chains or map the world, Trimble is at the forefront, driving productivity and progress.

Dynamic Robot Systems Group

The Dynamic Robot Systems Group (DRS) is a research group of the University of Oxford Robotics Institute (ORI). They research mapping, 3D perception, and navigation for dynamically moving robots such as legged robots, autonomous vehicles, aerial robotics, and handheld mapping systems with a strong emphasis on field operation. The group’s research focuses on sensor fusion between cameras, lidars, and other sensors using probabilistic fusion as well as modern machine learning systems.

Vision & Robotics

Vision & Robotics develops advanced software solutions for spatial perception, localization, SLAM, and data interpretation in real-world environments. These capabilities are offered as integrable solutions to customers across robotics, construction, and industrial sectors. In addition, the company contributes to research and innovation initiatives such as the Hilti SLAM Challenge, providing reference trajectories to support the competition.

Australian Robotic Inspection and Asset Management Hub (ARIAM)

ARIAM Hub is an ARC Research hub hosted by the University of Sydney and in partnership with Queensland University of Technology and The Australian National University. ARIAM aims to transform the way assets and infrastructure are managed through the development of intelligent robotic systems with new capabilities for inspection, monitoring, maintenance, and optimization. We share a vision for a future of robots equipped with the ability to autonomously collect data for integration into a digital twin that provides a real-time representation of the true state of a physical asset.