Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas
    ( 2020)
    Zhu, Jingwei
    ;
    Gehrung, Joachim
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    Huang , Rong
    ;
    Borgmann, Björn
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    Hoegner, Ludwig
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    Zhenghao, Sun
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    Hebel, Marcus orcid-logo
    ;
    XU, Yusheng
    ;
    Stilla, Uwe
    In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.
  • Publication
    Self-Calibration for the Time-of-Arrival Positioning
    ( 2020)
    Sidorenko, Juri
    ;
    Schatz, Volker
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    Bulatov, Dimitri
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    Scherer-Negenborn, Norbert
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    Arens, Michael
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    Hugentobler, Urs
    Self-calibration of time-of-arrival positioning systems is made difficult by the non-linearity of the relevant set of equations. This work applies dimension lifting to this problem. The objective function is extended by an additional dimension to allow the dynamics of the optimization to avoid local minima. Next to the usual numerical optimization, a partially analytical method is suggested, which makes the system of equations overdetermined proportionally to the number of measurements. Results with the lifted objective function are compared to those with the unmodified objective function. For evaluation purposes, the fractions of convergence to local minima are determined, for both synthetic data with random geometrical constellations and real measurements with a reasonable constellation of base stations. It is shown that the lifted objective function provides improved convergence in all cases, often significantly so.
  • Publication
    Change Detection and Deformation Analysis based on Mobile Laser Scanning Data of Urban Areas
    ( 2020)
    Gehrung, Joachim
    ;
    Hebel, Marcus orcid-logo
    ;
    Arens, Michael
    ;
    Stilla, Uwe
    Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.