Alcalde, Lucio

Alliez, Pierre

Brilakis, Ioannis

Bromley, Antony

Chee, Stanley

Fathy, Yasmin

Pluta, Kacper

Trzeciak, Maciej

Hal-Diderot

ConSLAM: Periodically Collected Real-World Construction Dataset for SLAM

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HAL Id: hal-03883862https://hal.inria.fr/hal-03883862Submitted on 4 Dec 2022HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.ConSLAM: Periodically Collected Real-WorldConstruction Dataset for SLAMMaciej Trzeciak, Kacper Pluta, Yasmin Fathy, Lucio Alcalde, Stanley Chee,Antony Bromley, Ioannis Brilakis, Pierre AlliezTo cite this version:Maciej Trzeciak, Kacper Pluta, Yasmin Fathy, Lucio Alcalde, Stanley Chee, et al.. ConSLAM: Peri-odically Collected Real-World Construction Dataset for SLAM. European Conference on ComputerVision, Oct 2022, Tel Aviv, Israel. ￿hal-03883862￿ConSLAM: Periodically Collected Real-World Construction Dataset for SLAMMaciej Trzeciak, Kacper Pluta, Yasmin Fathy, Lucio Alcalde, Stanley Chee, Antony Bromley, Ioannis Brilakis and Pierre AlliezChallengesOur Approach- The digitization of the geometry of existing infrastructure assets is a crucial step for creating an effective Digital Twin- SLAM algorithms utilized by mobile scanners are not yet accurate enough to meet the requirements of demanding use cases in the construction industry - Available datasets for SLAM algorithms were not collected on construction sites hence we do not know what their performance there is- There is no dataset collected periodically which would reflect real-world use cases such as progress monitoring During scanning, the recorded streams of data were time synchronized and monitored.We used our prototypical hand-held scanner to collect the five sequences of data and a static Terrestrial Laser Scanner (TLS) to collect corresponding ground-truth scans.LiDAR points can also be projected onto the corresponding images because we provide extrinsic calibration matrices between our sensors.We extend a popular software package (evo) to automatically measure the accuracy of SLAM algorithms against our trajectory and we show that A-LOAM and LIO-SAM (two popular odometry/SLAM algorithms) are subject to drift.Practical Application (1/2): Evaluation of SLAM trajectoryThe DatasetPractical Application (2/2):LiDAR projected onto images

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ConSLAM: Periodically Collected Real-World Construction Dataset for SLAM

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INRIA a CCSD electronic archive server