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Ergebnisse nach Hochschule und Institut
Publikation Implementation and first evaluation of an indoor mapping application using smartphones and frameworks(2019) Hasler, Oliver; Blaser, Stefan; Nebiker, StephanIn this paper, we present the implementation of a smartphone-based indoor mobile mapping application based on an augmented reality (AR) framework and a subsequent performance evaluation in demanding indoor environments. The implementation runs on Android and iOS devices and demonstrates the great potential of smartphone-based 3D mobile mapping. The application includes several functionalities such as device tracking, coordinate, and distance measuring as well as capturing georeferenced imagery. We evaluate our prototype system by comparing measured points from the tracked device with ground control points in an indoor environment with two different campaigns. The first campaign consists of an open, one-way trajectory whereas the second campaign incorporates a loop closure. In the second campaign, the underlying AR framework successfully recognized the start location and correctly repositioned the device. Our results show that the absolute 3D accuracy of device tracking with a standard smartphone is around 1% of the travelled distance and that the local 3D accuracy reaches sub-decimetre level.04B - Beitrag KonferenzschriftPublikation Open urban and forest datasets from a high-performance mobile mapping backpack. A contribution for advancing the creation of digital city twins(International Society of Photogrammetry and Remote Sensing, 2021) Blaser, Stefan; Meyer, Jonas; Nebiker, StephanWith 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-datasets04B - Beitrag KonferenzschriftPublikation AI-based 3D detection of parked vehicles on a mobile mapping platform using edge computing(2022) Meyer, Jonas; Blaser, Stefan; Nebiker, StephanIn this paper we present an edge-based hardware and software framework for the 3D detection and mapping of parked vehicles on a mobile mapping platform for the use case of on-street parking statistics. First, we investigate different point cloud-based 3D object detection methods on our extremely dense and noisy depth maps obtained from low-cost RGB-D sensors to find a suitable object detector and determine the optimal preparation of our data. We then retrain the chosen object detector to detect all types of vehicles, rather than standard cars only. Finally, we design and develop a software framework integrating the newly trained object detector. By repeating the parking statistics of our previous work (Nebiker et al., 2021), our software is tested regarding the detection accuracy. With our edge-based framework, we achieve a precision and recall of 100% and 98% respectively on any parking configuration and vehicle type, outperforming all other known work on on-street parking statistics. Furthermore, our software is evaluated in terms of processing speed and volume of generated data. While the processing speed reaches only 1.9 frames per second due to limited computing resources, the amount of data generated is just 0.25 KB per frame.04B - Beitrag KonferenzschriftPublikation Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras. The use case of on-street parking statistics(MDPI, 2021) Nebiker, Stephan; Meyer, Jonas; Blaser, Stefan; Ammann, Manuela; Rhyner, Severin EricA 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.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Robust and accurate image-based georeferencing exploiting relative orientation constraints(Copernicus, 2018) Cavegn, Stefan; Blaser, S.; Nebiker, Stephan; Haala, N.Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2–3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Development of a portable high performance mobile mapping system using the robot operating system(Copernicus, 2018) Blaser, Stefan; Cavegn, Stefan; Nebiker, StephanThe rapid progression in digitalization in the construction industry and in facility management creates an enormous demand for the efficient and accurate reality capturing of indoor spaces. Cloud-based services based on georeferenced metric 3D imagery are already extensively used for infrastructure management in outdoor environments. The goal of our research is to enable such services for indoor applications as well. For this purpose, we designed a portable mobile mapping research platform with a strong focus on acquiring accurate 3D imagery. Our system consists of a multi-head panorama camera in combination with two multi-profile LiDAR scanners and a MEMS-based industrial grade IMU for LiDAR-based online and offline SLAM. Our modular implementation based on the Robot Operating System enables rapid adaptations of the sensor configuration and the acquisition software. The developed workflow provides for completely GNSS-independent data acquisition and camera pose estimation using LiDAR-based SLAM. Furthermore, we apply a novel image-based georeferencing approach for further improving camera poses. First performance evaluations show an improvement from LiDAR-based SLAM to image-based georeferencing by an order of magnitude: from 10–13 cm to 1.3–1.8 cm in absolute 3D point accuracy and from 8–12 cm to sub-centimeter in relative 3D point accuracy.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Image-based reality-capturing and 3D modelling for the creation of VR cycling simulations(Copernicus, 2021) Wahbeh, Wissam; Ammann, Manuela; Nebiker, Stephan; van Eggermond, Michael; Erath, AlexanderWith this paper, we present a novel approach for efficiently creating reality-based, high-fidelity urban 3D models for interactive VR cycling simulations. The foundation of these 3D models is accurately georeferenced street-level imagery, which can be captured using vehicle-based or portable mapping platforms. Depending on the desired type of urban model, the street-level imagery is either used for semi-automatically texturing an existing city model or for automatically creating textured 3D meshes from multi-view reconstructions using commercial off-the-shelf software. The resulting textured urban 3D model is then integrated with a real-time traffic simulation solution to create a VR framework based on the Unity game engine. Subsequently, the resulting urban scenes and different planning scenarios can be explored on a physical cycling simulator using a VR helmet or viewed as a 360-degree or conventional video. In addition, the VR environment can be used for augmented reality applications, e.g., mobile augmented reality maps. We apply this framework to a case study in the city of Berne to illustrate design variants of new cycling infrastructure at a major traffic junction to collect feedback from practitioners about the potential for practical applications in planning processes.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Portable image-based high performance mobile mapping system in underground environments. System configuration and performance evalutation.(Copernicus, 2019) Blaser, S.; Nebiker, Stephan; Wisler, D.The progression in urbanization increases the need for different types of underground infrastructure. Consequently, infrastructure and life cycle management are rapidly gaining in importance. Mobile reality capturing systems and cloud-based services exploiting georeferenced metric 3D imagery are already extensively used for infrastructure management in outdoor environments. These services minimise dangerous and expensive field visits or measurement campaigns. In this paper, we introduce the BIMAGE Backpack, a portable image-based mobile mapping system for 3D data acquisition in indoor environments. The system consists of a multi-head panorama camera, two multi-profile laser scanners and an inertial measurement unit. With this system, we carried out underground measurement campaigns in the Hagerbach Test Gallery, located in Flums Hochwiese, Switzerland. For our performance evaluations in two different tunnel sections, we employed LiDAR SLAM as well as advanced image-based georeferencing. The obtained absolute accuracies were in the range from 6.2 to 7.4 cm. The relative accuracy, which for many applications is even more important, was in the range of 2–6 mm. These figures demonstrate an accuracy improvement of the subsequent image-based georeferencing over LiDAR SLAM by about an order of magnitude. The investigations show the application potential of image-based portable mobile mapping systems for infrastructure inventory and management in large underground facilities.01A - Beitrag in wissenschaftlicher ZeitschriftItem Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras. The use case of on-street parking statistics(MDPI, 05.08.2021) Nebiker, Stephan; Meyer, Jonas; Blaser, Stefan; Ammann, Manuela ; Rhyner, SeverinA 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.01A - Beitrag in wissenschaftlicher Zeitschrift