Hilti x Trimble SLAM Challenge 2026
Related docs: SLAM Benchmarking Metrics and Datasets, OpenVINS, ORB-SLAM2 and ORB-SLAM3, SVO, Cartographer 3D, and AV / Indoor / Outdoor SLAM Decision Matrix.
Last updated: 2026-05-23
Executive Summary
The Hilti x Trimble SLAM Challenge 2026 is a construction-site visual-inertial benchmark from Hilti, Trimble, and the University of Oxford Dynamic Robot Systems Group. It differs from the older Hilti SLAM challenge line because it centers on a 360-degree camera with embedded IMU measurements and optional floor-plan priors, with separate tasks for free-frame SLAM and localization in the floor-plan reference frame.
This is a strong reference for industrial indoor localization because it tests active construction-site geometry: repetitive texture, changing lighting, partially built structures, floor-plan mismatch, rolling-shutter 360 imagery, dynamic initialization, and hidden ground-truth evaluation. It is not a direct AV benchmark, but it transfers well to warehouses, terminals, depots, hangars, construction sites, and other sites where a robot may have building plans but not a finished, static environment.
What It Contains
The official challenge page and repository describe:
- 30 public recordings across multiple floors and underground levels.
- Two challenge categories: SLAM in any reference frame and localization in the floor-plan map frame.
- 360-degree visual-inertial data from an Insta360 One-RS 1-Inch Edition camera.
- Embedded IMU measurements from the camera.
- Floor-plan images, including variants with and without windows.
- LiDAR-inertial ground truth generated by a rigidly attached Hesai XT32-based mapping device.
- ROS 2 bags and helper tools for image decompression, image stitching, image inversion, floor-plan map serving, OpenVINS examples, and Stella-VSLAM examples.
The raw LiDAR data used for ground truth is not part of the released challenge input. That makes the benchmark specifically about visual-inertial SLAM/localization from the camera rig, with floor-plan context for the localization task.
Evaluation Model
| Signal | Challenge behavior | Why it matters |
|---|---|---|
| Trajectory coverage | Runs below 99 percent matched-pose coverage receive zero for that run | Prevents methods from producing accurate fragments while failing most of the route. |
| Exponential position score | Errors are converted to a bounded score per pose and summed by run | Rewards accurate localization while making large errors visibly costly. |
| Hidden evaluation | Some run details are withheld from public feedback | Reduces overfitting to the evaluation set. |
| Floor-plan localization task | Camera pose must be expressed in a map frame derived from floor-plan pixels | Tests the practical problem of localizing against imperfect building priors. |
| No post-hoc scale adjustment | The challenge notes that scale is not adjusted during scoring | Forces visual-inertial systems to maintain metric scale. |
Domain Fit
| Domain | Fit | Note |
|---|---|---|
| Construction-site robotics | Strong | This is the benchmark's primary domain. |
| Warehouses and depots | Strong to conditional | Floor-plan priors, repetitive interiors, and changing layouts transfer well. |
| Airport terminals / hangars | Conditional | Useful for indoor/hangar localization; not representative of open aprons. |
| Road AV | Weak | Vehicle dynamics, weather, traffic, HD maps, and sensor suites differ. |
| Airside apron | Weak to conditional | Useful only for indoor or terminal-edge visual-inertial failure analysis. |
Failure Modes It Exposes
- VIO initialization while the operator or platform is already moving.
- Rolling-shutter and dual-fisheye geometry artifacts from 360-degree cameras.
- Drift when floor-plan priors disagree with the as-built construction state.
- False localization in repetitive corridors, columns, and unfinished rooms.
- Scale inconsistency when the method cannot maintain metric visual-inertial pose.
- Tooling assumptions that fail on ROS 2 bag formats, compressed images, or unusual camera models.
Implementation Notes
- Keep SLAM and localization task results separate; one estimates trajectory in any frame, the other must align to the floor-plan map frame.
- Report whether floor-plan priors are used, and how map mismatch is handled.
- Treat the provided floor plans as imperfect priors, not surveyed truth.
- Use the OpenVINS and Stella-VSLAM examples as reproducibility baselines, not as proof that a method is robust.
- Note the CC BY-NC-SA 3.0 dataset/benchmark license before using data in commercial or redistribution workflows.
Limitations
- The benchmark is construction-site focused, not road, airside-apron, port, mining, or agricultural autonomy.
- The challenge deadline was 2026-05-15 for prize eligibility, though the evaluation system remains useful as a reference.
- Most ground truth is hidden, and raw LiDAR ground-truth acquisition data is not released as normal benchmark input.
- The camera is a consumer 360-degree device, so results may not transfer directly to calibrated industrial multi-camera rigs.
- Dataset licensing is noncommercial share-alike.
Sources
- Hilti x Trimble Challenge 2026 official page: https://hilti-trimble-challenge.com/
- Official challenge repository: https://github.com/Hilti-Research/hilti-trimble-slam-challenge-2026
- Evaluation server: https://submit.hilti-challenge.com/
- Dataset license statement on official page: https://hilti-trimble-challenge.com/
- OpenVINS documentation referenced by the challenge tooling: https://docs.openvins.com/