aiqbench analysis
This article presents a specialized dataset for visual-inertial SLAM and floor plan localization in construction environments, addressing the need for cost-effective alternatives to LiDAR. By providing ground truth trajectories from high-quality systems alongside real-world challenges like moving workers and variable lighting, it directly supports advancements in autonomous robotics and spatial AI applications.
Hilti-Trimble-Oxford Dataset: 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization
arXiv:2607.06464v2 Announce Type: replace 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 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 ten floors over an eight-month period of the construction project. Ground truth trajectories are 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 localization, which suggests that this task receives less attention from researchers and motivates further exploration through proposed dataset and challenge, available at: https://hilti-trimble-challenge.com/dataset-2026.
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 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 ten floors over an eight-month period of the construction project. Ground truth trajectories are 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 localization, which suggests that this task receives less attention from researchers and motivates further exploration through proposed dataset and challenge, available at: this https URL.
Robotics (cs.RO)
arXiv:2607.06464 [cs.RO]
(or arXiv:2607.06464v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.06464 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 308 words