[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83101-en":3,"doc-seo-83101-105":29,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83101,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Hilti-Trimble-Oxford Dataset 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization","The Hilti-Trimble-Oxford Dataset addresses automated construction progress monitoring by enabling accurate visual-inertial SLAM and floor-plan-referenced localization without relying on costly LiDAR. A high-quality dataset is collected at an active site under realistic conditions including variable lighting, moving workers, fast motions, and repetitive structures. It provides 30 synchronized 360-degree visual-inertial sequences across seven floors over eight months, with ground-truth trajectories from a LiDAR-inertial SLAM system and results from an open benchmarking challenge.","Hilti-Trimble-Oxford Dataset: 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization  \nSamuele Centanni 1∗, Yuhao Zhang2∗, Yifu Tao2 , Julien Kindle 1 ,3 , Frank Neuhaus4 , Tilman Koß4 Aryaman Patel5 , Michael Helmberger1 , Emilia Szyma´nska 1 , Torben Gräber1 , Maurice Fallon2  \narXiv :2607 .06464v 1 [ cs .RO] 7 Jul 2026  \nAbstract—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 LiDARinertial 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-challen](https://hilti-trimble-challen)[ge.com/dataset-2026](ge.com/dataset-2026).  \nI. INTRODUCTION  \nThe buildings and construction industry accounts for approximately 11-13% of global GDP [1] . It is, however, a sector that has seen minimal improvements to productivity, in part due to the limited digitization of work processes, rework due to defects and quality deviations, occupational safety hazards, and demographic changes in the construction workforce [2] . These challenges motivate automated methods for tracking the progress of construction to provide site managers with timely insights into the as-is state of the project.  \nA common approach to construction site monitoring is the regular mapping of the environment using a variety of sensors such as LiDAR, RGB cameras, or terrestrial laser  \n∗Equal contribution  \n1Hilti AG, Corporate Research & Technology, Schaan, Liechtenstein  \n2University of Oxford, Dynamic Robot Systems Group, Oxford, UK  \n3ETH Zürich, Robotics Systems Lab, Zürich, Switzerland  \n4Vision & Robotics GmbH, Koblenz, Germany  \n5Trimble Inc., Denver, USA  \nFig. 1: The Hilti-Trimble-Oxford Dataset, consisting of 360-degree videos, IMU measurements and floor plans, allows for benchmarking algorithms for SLAM and floor-plan-referenced localization.  \nscanners [3, 4] . These sensors may be operated manually or mounted on robotic platforms, including legged robots, drones, and wheeled platforms [5, 6] . The usefulness of the resulting maps depends on the extent to which they can support change detection across consecutive mapping sessions or comparison against architectural models [7] . Detecting errors or deviations early can lead to substantial savings in both cost and time. The maps can also serve as documentary evidence of the building project [8] .  \nThe core technology allowing for the generation of such maps is Simultaneous Localization and Mapping (SLAM),","cbCailULvz0TsC9N","https://ap.wps.com/l/cbCailULvz0TsC9N","pdf",8524829,1,9,"English","en",105,"# Introduction\n## Construction monitoring and mapping needs\n## SLAM and camera-based mapping\n## Floor-plan-referenced localization and motivation\n## Dataset and benchmark contributions","[{\"question\":\"What dataset is presented and what problem does it support?\",\"answer\":\"The document presents the Hilti-Trimble-Oxford Dataset to support automated construction progress monitoring through benchmarking of visual-inertial SLAM and floor-plan-referenced localization.\"},{\"question\":\"What sensors and data modalities are included in the dataset?\",\"answer\":\"The dataset includes 360-degree video sequences synchronized with IMU measurements and corresponding 2D floor plans, with ground-truth trajectories obtained using a high-accuracy LiDAR-inertial SLAM system.\"},{\"question\":\"What realistic challenges are captured during data collection?\",\"answer\":\"The sequences reflect variable lighting conditions, moving workers, fast motions, and repetitive structures encountered in an active construction environment.\"},{\"question\":\"How was the benchmark evaluated and what did participation indicate?\",\"answer\":\"An open research challenge evaluated visual SLAM and localization systems; participation was much higher for SLAM (62 teams) than for floor-plan-referenced localization (22 teams), highlighting differing maturity and the higher difficulty of localization.\"}]",1784185259,23,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":27},"hilti-trimble-oxford-dataset-360-visual-inertial-benchmark-with-floor-plan-priors-for-slam-and-localization","",{"@graph":35,"@context":89},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/hilti-trimble-oxford-dataset-360-visual-inertial-benchmark-with-floor-plan-priors-for-slam-and-localization/83101/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What dataset is presented and what problem does it support?","Question",{"text":75,"@type":76},"The document presents the Hilti-Trimble-Oxford Dataset to support automated construction progress monitoring through benchmarking of visual-inertial SLAM and floor-plan-referenced localization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What sensors and data modalities are included in the dataset?",{"text":80,"@type":76},"The dataset includes 360-degree video sequences synchronized with IMU measurements and corresponding 2D floor plans, with ground-truth trajectories obtained using a high-accuracy LiDAR-inertial SLAM system.",{"name":82,"@type":73,"acceptedAnswer":83},"What realistic challenges are captured during data collection?",{"text":84,"@type":76},"The sequences reflect variable lighting conditions, moving workers, fast motions, and repetitive structures encountered in an active construction environment.",{"name":86,"@type":73,"acceptedAnswer":87},"How was the benchmark evaluated and what did participation indicate?",{"text":88,"@type":76},"An open research challenge evaluated visual SLAM and localization systems; 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