Abstract
Abstract. With this contribution, we describe and publish two high-quality street-level datasets, captured with a portable high-performance Mobile Mapping System (MMS). The datasets will be freely available for scientific use. Both datasets, from a city centre and a forest represent area-wide street-level reality captures which can be used e.g. for establishing cloud-based frameworks for infrastructure management as well as for smart city and forestry applications. The quality of these data sets has been thoroughly evaluated and demonstrated. For example, georeferencing accuracies in the centimetre range using these datasets in combination with image-based georeferencing have been achieved. Both high-quality multi sensor system street-level datasets are suitable for evaluating and improving methods for multiple tasks related to high-precision 3D reality capture and the creation of digital twins. Potential applications range from localization and georeferencing, dense image matching and 3D reconstruction to combined methods such as simultaneous localization and mapping and structure-from-motion as well as classification and scene interpretation. Our dataset is available online at: https://www.fhnw.ch/habg/bimage-datasets
Highlights
The ongoing progress in digitalization leads to massive transformations and innovations in infrastructure management
Both datasets contain a) image data with undistorted equidistant and anonymized images from the individual panoramic camera heads as well as the image timestamps, b) light detection and ranging (LiDAR) data represented as timestamped point clouds in the sensor coordinate frame, c) navigation data with global navigation satellite system (GNSS) raw observations from the BIMAGE Backpack as well as from the reference station and inertial measurement unit (IMU) raw data
LiDAR Simultaneous Localization and Mapping (SLAM) is promising on both datasets because the LiDAR acquisition data frequency is higher, and the processing effort lower compared to images and visual SLAM
Summary
The ongoing progress in digitalization leads to massive transformations and innovations in infrastructure management. Mobile mapping systems (MMS) hold the potential to provide such data in a cost-effective manner. Blaser et al (2018) present the development of a portable image-based indoor MMS and provide accuracy analysis in indoor environment with promising results within the centimetre range. Blaser et al (2020) extended the portable MMS with a tactical grade inertial measurement unit (IMU) for indoor and outdoor use. They conducted performance evaluations using three independent georeferencing methods in challenging outdoor test sites not accessible to vehicles and achieved accuracies in the centimetre range. We consider that our challenging datasets could help to accelerate the development of novel methods in the field of mobile 3D reality capture and smart city. We show initial research and point out its potential and show possible applications
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