Hilti-Trimble-Oxford Dataset: 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization

2026-07-07

AI Summary

This work proposes a high-precision localization method for construction sites that integrates 360° visual-inertial sensing with floorplan priors to address challenges such as drastic illumination changes, dynamic disturbances, rapid motion, and repetitive structures. The authors present the first long-term, multi-floor 360° visual-inertial benchmark dataset collected in real-world construction environments, comprising 30 sequences across seven floors over eight months, with ground-truth trajectories generated by LiDAR-inertial SLAM. This dataset has already underpinned an open challenge that attracted 84 participating teams, revealing critical performance limitations of current SLAM and localization algorithms in construction scenarios and establishing a foundational benchmark for research in automated construction monitoring.

Abstract

Automated progress monitoring on construction sites is an active area of research and development. Robot and human-carried mapping systems have been developed to build 3D maps of building and infrastructure projects. While LiDAR-based mapping systems achieve high accuracy, the cost of LiDAR can be prohibitive. Consumer-grade cameras with wide field of view ("360 cameras") combined with embedded inertial measurement units (IMUs) provide a cost-effective alternative. To support change detection and progress monitoring, highly accurate visual Simultaneous Localization and Mapping (SLAM) and floor plan-referenced localization systems are required. In this paper we present a high-quality dataset collected at an active construction site, which captures realistic challenges such as variable lighting conditions, moving workers, fast motions, and repetitive structures. The dataset offers thirty visual-inertial sequences recorded across seven floors over an eight-month period of the construction project. Ground truth trajectories were collected using a high quality LiDAR-inertial SLAM system rigidly attached to the 360 camera. Additionally, we report the results of an open research challenge evaluating the best visual SLAM and localization systems from around the world. The Challenge attracted substantially higher participation in SLAM, with 62 teams compared to 22 in floor-plan-referenced localization, reflecting the broader maturity of SLAM methods. The higher errors in localization further highlight the difficulty of this task in construction and point to the need for continued research, which this dataset is intended to support. The dataset and the benchmark are publicly available at: https://hilti-trimble-challenge.com/dataset-2026.

Problem

• visual-inertial SLAM
• floor plan-referenced localization
• construction site
• progress monitoring
• 360 camera

Innovation

• 360 visual-inertial SLAM
• floor plan priors
• construction site localization
• benchmark dataset
• LiDAR-inertial ground truth