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M. Peter
Independent Researcher, 46397 Bocholt, Germany

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Journal article
Published: 13 April 2019 in Remote Sensing
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Indoor mobile mapping systems are important for a wide range of applications starting from disaster management to straightforward indoor navigation. This paper presents the design and performance of a low-cost backpack indoor mobile mapping system (ITC-IMMS) that utilizes a combination of laser range-finders (LRFs) to fully recover the 3D building model based on a feature-based simultaneous localization and mapping (SLAM) algorithm. Specifically, we use robust planar features. These are advantageous, because oftentimes the final representation of the indoor environment is wanted in a planar form, and oftentimes the walls in an indoor environment physically have planar shapes. In order to understand the potential accuracy of our indoor models and to assess the system’s ability to capture the geometry of indoor environments, we develop novel evaluation techniques. In contrast to the state-of-the-art evaluation methods that rely on ground truth data, our evaluation methods can check the internal consistency of the reconstructed map in the absence of any ground truth data. Additionally, the external consistency can be verified with the often available as-planned state map of the building. The results demonstrate that our backpack system can capture the geometry of the test areas with angle errors typically below 1.5° and errors in wall thickness around 1 cm. An optimal configuration for the sensors is determined through a set of experiments that makes use of the developed evaluation techniques.

ACS Style

Samer Karam; George Vosselman; Michael Peter; Siavash Hosseinyalamdary; Ville Lehtola. Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System. Remote Sensing 2019, 11, 905 .

AMA Style

Samer Karam, George Vosselman, Michael Peter, Siavash Hosseinyalamdary, Ville Lehtola. Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System. Remote Sensing. 2019; 11 (8):905.

Chicago/Turabian Style

Samer Karam; George Vosselman; Michael Peter; Siavash Hosseinyalamdary; Ville Lehtola. 2019. "Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System." Remote Sensing 11, no. 8: 905.

Journal article
Published: 15 November 2018 in Remote Sensing
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State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases.

ACS Style

Ahmed Elseicy; Shayan Nikoohemat; Michael Peter; Sander Oude Elberink. Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory. Remote Sensing 2018, 10, 1815 .

AMA Style

Ahmed Elseicy, Shayan Nikoohemat, Michael Peter, Sander Oude Elberink. Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory. Remote Sensing. 2018; 10 (11):1815.

Chicago/Turabian Style

Ahmed Elseicy; Shayan Nikoohemat; Michael Peter; Sander Oude Elberink. 2018. "Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory." Remote Sensing 10, no. 11: 1815.

Journal article
Published: 07 November 2018 in Remote Sensing
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The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes.

ACS Style

Shayan Nikoohemat; Michael Peter; Sander Oude Elberink; George Vosselman. Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories. Remote Sensing 2018, 10, 1754 .

AMA Style

Shayan Nikoohemat, Michael Peter, Sander Oude Elberink, George Vosselman. Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories. Remote Sensing. 2018; 10 (11):1754.

Chicago/Turabian Style

Shayan Nikoohemat; Michael Peter; Sander Oude Elberink; George Vosselman. 2018. "Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories." Remote Sensing 10, no. 11: 1754.

Journal article
Published: 05 June 2018 in Sensors
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Indoor space subdivision is an important aspect of scene analysis that provides essential information for many applications, such as indoor navigation and evacuation route planning. Until now, most proposed scene understanding algorithms have been based on whole point clouds, which has led to complicated operations, high computational loads and low processing speed. This paper presents novel methods to efficiently extract the location of openings (e.g., doors and windows) and to subdivide space by analyzing scanlines. An opening detection method is demonstrated that analyses the local geometric regularity in scanlines to refine the extracted opening. Moreover, a space subdivision method based on the extracted openings and the scanning system trajectory is described. Finally, the opening detection and space subdivision results are saved as point cloud labels which will be used for further investigations. The method has been tested on a real dataset collected by ZEB-REVO. The experimental results validate the completeness and correctness of the proposed method for different indoor environment and scanning paths.

ACS Style

Yi Zheng; Michael Peter; Ruofei Zhong; Sander Oude Elberink; Quan Zhou. Space Subdivision in Indoor Mobile Laser Scanning Point Clouds Based on Scanline Analysis. Sensors 2018, 18, 1838 .

AMA Style

Yi Zheng, Michael Peter, Ruofei Zhong, Sander Oude Elberink, Quan Zhou. Space Subdivision in Indoor Mobile Laser Scanning Point Clouds Based on Scanline Analysis. Sensors. 2018; 18 (6):1838.

Chicago/Turabian Style

Yi Zheng; Michael Peter; Ruofei Zhong; Sander Oude Elberink; Quan Zhou. 2018. "Space Subdivision in Indoor Mobile Laser Scanning Point Clouds Based on Scanline Analysis." Sensors 18, no. 6: 1838.

Editorial
Published: 17 April 2018 in Journal of Sensors
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ACS Style

Jacky C. K. Chow; Michael Peter; Marco Scaioni; Mohannad Al-Durgham. Indoor Tracking, Mapping, and Navigation: Algorithms, Technologies, and Applications. Journal of Sensors 2018, 2018, 1 -3.

AMA Style

Jacky C. K. Chow, Michael Peter, Marco Scaioni, Mohannad Al-Durgham. Indoor Tracking, Mapping, and Navigation: Algorithms, Technologies, and Applications. Journal of Sensors. 2018; 2018 ():1-3.

Chicago/Turabian Style

Jacky C. K. Chow; Michael Peter; Marco Scaioni; Mohannad Al-Durgham. 2018. "Indoor Tracking, Mapping, and Navigation: Algorithms, Technologies, and Applications." Journal of Sensors 2018, no. : 1-3.

Journal article
Published: 12 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Documentation of the “as-built” state of building interiors has gained a lot of interest in the recent years. Various data acquisition methods exist, e.g. the extraction from photographed evacuation plans using image processing or, most prominently, indoor mobile laser scanning. Due to clutter or data gaps as well as errors during data acquisition and processing, automatic reconstruction of CAD/BIM-like models from these data sources is not a trivial task. Thus it is often tried to support reconstruction by general rules for the perpendicularity and parallelism which are predominant in man-made structures. Indoor environments of large, public buildings, however, often also follow higher-level rules like symmetry and repetition of e.g. room sizes and corridor widths. In the context of reconstruction of city city elements (e.g. street networks) or building elements (e.g. fac¸ade layouts), formal grammars have been put to use. In this paper, we describe the use of Lindenmayer systems - which originally have been developed for the computer-based modelling of plant growth - to model and reproduce the layout of indoor environments in 2D.

ACS Style

M. Peter. MODELLING OF INDOOR ENVIRONMENTS USING LINDENMAYER SYSTEMS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W7, 385 -390.

AMA Style

M. Peter. MODELLING OF INDOOR ENVIRONMENTS USING LINDENMAYER SYSTEMS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W7 ():385-390.

Chicago/Turabian Style

M. Peter. 2017. "MODELLING OF INDOOR ENVIRONMENTS USING LINDENMAYER SYSTEMS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7, no. : 385-390.

Journal article
Published: 12 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Automated generation of 3D indoor models from point cloud data has been a topic of intensive research in recent years. While results on various datasets have been reported in literature, a comparison of the performance of different methods has not been possible due to the lack of benchmark datasets and a common evaluation framework. The ISPRS benchmark on indoor modelling aims to address this issue by providing a public benchmark dataset and an evaluation framework for performance comparison of indoor modelling methods. In this paper, we present the benchmark dataset comprising several point clouds of indoor environments captured by different sensors. We also discuss the evaluation and comparison of indoor modelling methods based on manually created reference models and appropriate quality evaluation criteria. The benchmark dataset is available for download at: http://www2.isprs.org/commissions/comm4/wg5/benchmark-on-indoor-modelling.html.

ACS Style

K. Khoshelham; L. Díaz Vilariño; M. Peter; Z. Kang; Debaditya Acharya. THE ISPRS BENCHMARK ON INDOOR MODELLING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W7, 367 -372.

AMA Style

K. Khoshelham, L. Díaz Vilariño, M. Peter, Z. Kang, Debaditya Acharya. THE ISPRS BENCHMARK ON INDOOR MODELLING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W7 ():367-372.

Chicago/Turabian Style

K. Khoshelham; L. Díaz Vilariño; M. Peter; Z. Kang; Debaditya Acharya. 2017. "THE ISPRS BENCHMARK ON INDOOR MODELLING." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7, no. : 367-372.

Proceedings article
Published: 01 March 2017 in 2017 Joint Urban Remote Sensing Event (JURSE)
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The acquisition and processing techniques of Airborne Laser Scanning (ALS) data have improved rapidly in recent years. Due to the relative high costs of laser scanning, we want to explore the potential of detecting changes and updating Digital Surface Models using point clouds derived from Dense Image Matching (DIM). The prerequisite of this work is to evaluate dense matching quality. In this paper, a workflow is designed to evaluate dense matching quality using planar roof segments. ALS data are taken as reference. The workflow can be divided into two steps: roof detection and quality evaluation. Two types of accuracy plots are depicted based on single DIM points and single segments. The experimental results show the point-to-plane residuals of DIM points are around 3 cm.

ACS Style

Zhenchao Zhang; Markus Gerke; Michael Peter; Michael Ying Yang; George Vosselman. Dense matching quality evaluation - an empirical study. 2017 Joint Urban Remote Sensing Event (JURSE) 2017, 1 -4.

AMA Style

Zhenchao Zhang, Markus Gerke, Michael Peter, Michael Ying Yang, George Vosselman. Dense matching quality evaluation - an empirical study. 2017 Joint Urban Remote Sensing Event (JURSE). 2017; ():1-4.

Chicago/Turabian Style

Zhenchao Zhang; Markus Gerke; Michael Peter; Michael Ying Yang; George Vosselman. 2017. "Dense matching quality evaluation - an empirical study." 2017 Joint Urban Remote Sensing Event (JURSE) , no. : 1-4.