ConSLAM: Construction Data Set for SLAM

Trzeciak, Maciej; Pluta, Kacper; Fathy, Yasmin; Alcalde, Lucio; Chee, Stanley; Bromley, Antony; Brilakis, Ioannis; Alliez, Pierre

doi:10.1061/jccee5.cpeng-5212

J. Comput. Civ. Eng.

2023

DOI: 10.1061/jccee5.cpeng-5212

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ConSLAM: Construction Data Set for SLAM

Maciej Trzeciak

Kacper Pluta

Yasmin Fathy

et al.

Abstract: This paper presents a dataset collected periodically on a construction site. The dataset aims to evaluate the performance of SLAM algorithms used by mobile scanners or autonomous robots. It includes ground-truth scans of a construction site collected using a terrestrial laser scanner along with five sequences of spatially registered and time-synchronized images, LiDAR scans and inertial data coming from our prototypical hand-held scanner. We also recover the ground-truth trajectory of the mobile scanner by reg… Show more

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Cited by 7 publications

References 26 publications

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MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle

Trybała,

Szrek,

Remondino

et al. 2023

PFG

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The research potential in the field of mobile mapping technologies is often hindered by several constraints. These include the need for costly hardware to collect data, limited access to target sites with specific environmental conditions or the collection of ground truth data for a quantitative evaluation of the developed solutions. To address these challenges, the research community has often prepared open datasets suitable for developments and testing. However, the availability of datasets that encompass truly demanding mixed indoor–outdoor and subterranean conditions, acquired with diverse but synchronized sensors, is currently limited. To alleviate this issue, we propose the MIN3D dataset (MultI-seNsor 3D mapping with an unmanned ground vehicle for mining applications) which includes data gathered using a wheeled mobile robot in two distinct locations: (i) textureless dark corridors and outside parts of a university campus and (ii) tunnels of an underground WW2 site in Walim (Poland). MIN3D comprises around 150 GB of raw data, including images captured by multiple co-calibrated monocular, stereo and thermal cameras, two LiDAR sensors and three inertial measurement units. Reliable ground truth (GT) point clouds were collected using a survey-grade terrestrial laser scanner. By openly sharing this dataset, we aim to support the efforts of the scientific community in developing robust methods for navigation and mapping in challenging underground conditions. In the paper, we describe the collected data and provide an initial accuracy assessment of some visual- and LiDAR-based simultaneous localization and mapping (SLAM) algorithms for selected sequences. Encountered problems, open research questions and areas that could benefit from utilizing our dataset are discussed. Data are available at https://3dom.fbk.eu/benchmarks.

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MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle

Trybała,

Szrek,

Remondino

et al. 2023

PFG

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SLAM2REF: advancing long-term mapping with 3D LiDAR and reference map integration for precise 6-DoF trajectory estimation and map extension

Vega-Torres,

Braun,

Borrmann

2024

Constr Robot

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This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework’s remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework’s significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://github.com/MigVega/SLAM2REF, Data: https://doi.org/10.14459/2024mp1743877.

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Automated vision-based construction progress monitoring in built environment through digital twin

Pal,

Lin,

Hsieh

et al. 2023

Developments in the Built Environment

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ConSLAM: Construction Data Set for SLAM

Cited by 7 publications

References 26 publications

MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle

MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle

SLAM2REF: advancing long-term mapping with 3D LiDAR and reference map integration for precise 6-DoF trajectory estimation and map extension

Automated vision-based construction progress monitoring in built environment through digital twin

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