2021, Blaser, Stefan, Meyer, Jonas, Nebiker, Stephan

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

2021, Nebiker, Stephan, Meyer, Jonas, Blaser, Stefan, Ammann, Manuela, Rhyner, Severin Eric

A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.