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The Hilti SLAM Challenge Dataset


Helmberger, Michael; Morin, Kristian; Berner, Beda; Kumar, Nitish; Cioffi, Giovanni; Scaramuzza, Davide (2022). The Hilti SLAM Challenge Dataset. IEEE Robotics and Automation Letters, 7(3):7518-7525.

Abstract

Research in Simultaneous Localization and Mapping (SLAM) has made outstanding progress over the past years. SLAM systems are nowadays transitioning from academic to real world applications. However, this transition has posed new demanding challenges in terms of accuracy and robustness. To develop new SLAM systems that can address these challenges, new datasets containing cutting-edge hardware and realistic scenarios are required. We propose the Hilti SLAM Challenge Dataset . Our dataset contains indoor sequences of offices, labs, and construction environments and outdoor sequences of construction sites and parking areas. All these sequences are characterized by featureless areas and varying illumination conditions that are typical in real-world scenarios and pose great challenges to SLAM algorithms that have been developed in confined lab environments. Accurate sparse ground truth, at millimeter level, is provided for each sequence. The sensor platform used to record the data includes a number of visual, lidar, and inertial sensors, which are spatially and temporally calibrated. The purpose of this dataset is to foster the research in sensor fusion to develop SLAM algorithms that can be deployed in tasks where high accuracy and robustness are required, e.g. , in construction environments. Many academic and industrial groups tested their SLAM systems on the proposed dataset in the Hilti SLAM Challenge . The results of the challenge, which are summarized in this paper, show that the proposed dataset is an important asset in the development of new SLAM algorithms that are ready to be deployed in the real-world.

Abstract

Research in Simultaneous Localization and Mapping (SLAM) has made outstanding progress over the past years. SLAM systems are nowadays transitioning from academic to real world applications. However, this transition has posed new demanding challenges in terms of accuracy and robustness. To develop new SLAM systems that can address these challenges, new datasets containing cutting-edge hardware and realistic scenarios are required. We propose the Hilti SLAM Challenge Dataset . Our dataset contains indoor sequences of offices, labs, and construction environments and outdoor sequences of construction sites and parking areas. All these sequences are characterized by featureless areas and varying illumination conditions that are typical in real-world scenarios and pose great challenges to SLAM algorithms that have been developed in confined lab environments. Accurate sparse ground truth, at millimeter level, is provided for each sequence. The sensor platform used to record the data includes a number of visual, lidar, and inertial sensors, which are spatially and temporally calibrated. The purpose of this dataset is to foster the research in sensor fusion to develop SLAM algorithms that can be deployed in tasks where high accuracy and robustness are required, e.g. , in construction environments. Many academic and industrial groups tested their SLAM systems on the proposed dataset in the Hilti SLAM Challenge . The results of the challenge, which are summarized in this paper, show that the proposed dataset is an important asset in the development of new SLAM algorithms that are ready to be deployed in the real-world.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Control and Systems Engineering
Physical Sciences > Biomedical Engineering
Physical Sciences > Human-Computer Interaction
Physical Sciences > Mechanical Engineering
Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Computer Science Applications
Physical Sciences > Control and Optimization
Physical Sciences > Artificial Intelligence
Scope:Discipline-based scholarship (basic research)
Language:English
Date:July 2022
Deposited On:26 Feb 2024 15:37
Last Modified:30 Apr 2024 01:52
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2377-3766
OA Status:Green
Publisher DOI:https://doi.org/10.1109/LRA.2022.3183759
  • Content: Accepted Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)