Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    The MODISSA testbed: A multi-purpose platform for the prototypical realization of vehicle-related applications using optical sensors
    ( 2021)
    Borgmann, Björn
    ;
    Schatz, Volker
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    Hammer, Marcus
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    Hebel, Marcus orcid-logo
    ;
    Arens, Michael
    ;
    Stilla, Uwe
    We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has successively been equipped with a variety of optical sensors over the past few years, and contains hardware for complete raw data acquisition, georeferencing, real-time data analysis, and immediate visualization on in-car displays. We demonstrate the capabilities of MODISSA by giving a deeper insight into experiments with its specific configuration in the scope of three different applications. Other research groups can benefit from these experiences when setting up their own mobile sensor system, especially regarding the selection of hardware and software, the knowledge of possible sources of error, and the handling of the acquired sensor data.
  • Publication
    DecaWave ultra-wideband warm-up error correction
    ( 2021)
    Sidorenko, Juri
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    Schatz, Volker
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    Scherer-Negenborn, Norbert
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    Arens, Michael
    ;
    Hugentobler, Urs
    In the field of indoor localization, ultra-wideband (UWB) technology is no longer dispensable. The market demands that the UWB hardware has to be cheap, precise and accurate. These requirements lead to the popularity of the DecaWave UWB system. The great majority of the publications about this system deals with the correction of the signal power, hardware delay or clock drift. It has traditionally been assumed that this error only appears at the beginning of the operation and is caused by the warm-up process of the crystal. In this article, we show that the warm-up error is influenced by the same error source as the signal power. To our knowledge, no scientific publication has explicitly examined the warm-up error before. This work aims to close this gap and, moreover, to present a solution which does not require any external measuring equipment and only has to be carried out once. It is shown that the empirically obtained warm-up correction curve increases the accuracy for the twoway- ranging (TWR) significantly.
  • Publication
    Information Acquisition on Pedestrian Movements in Urban Traffic with a Mobile Multi-Sensor System
    ( 2021)
    Borgmann, Björn
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    Hebel, Marcus orcid-logo
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    Arens, Michael
    ;
    Stilla, Uwe
    This paper presents an approach which combines LiDAR sensors and cameras of a mobile multi-sensor system to obtain information about pedestrians in the vicinity of the sensor platform. Such information can be used, for example, in the context of driver assistance systems. In the first step, our approach starts by using LiDAR sensor data to detect and track pedestrians, benefiting from LiDAR's capability to directly provide accurate 3D data. After LiDAR-based detection, the approach leverages the typically higher data density provided by 2D cameras to determine the body pose of the detected pedestrians. The approach combines several state-of-the-art machine learning techniques: it uses a neural network and a subsequent voting process to detect pedestrians in LiDAR sensor data. Based on the known geometric constellation of the different sensors and the knowledge of the intrinsic parameters of the cameras, image sections are generated with the respective regions of interest showing only the detected pedestrians. These image sections are then processed with a method for image-based human pose estimation to determine keypoints for different body parts. These keypoints are finally projected from 2D image coordinates to 3D world coordinates using the assignment of the original LiDAR points to a particular pedestrian.
  • Publication
    Self-Calibration for the Time Difference of Arrival Positioning
    ( 2020)
    Sidorenko, Juri
    ;
    Schatz, Volker
    ;
    Bulatov, Dimitri
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    Scherer-Negenborn, Norbert
    ;
    Arens, Michael
    ;
    Hugentobler, Urs
    The time-difference-of-arrival (TDOA) self-calibration is an important topic for many applications, such as indoor navigation. One of the most common methods is to perform nonlinear optimization. Unfortunately, optimization often gets stuck in a local minimum. Here, we propose a method of dimension lifting by adding an additional variable into the l2 norm of the objective function. Next to the usual numerical optimization, a partially-analytical method is suggested, which overdetermines the system of equations proportionally to the number of measurements. The effect of dimension lifting on the TDOA self-calibration is verified by experiments with synthetic and real measurements. In both cases, self-calibration is performed for two very common and often combined localization systems, the DecaWave Ultra-Wideband (UWB) and the Abatec Local Position Measurement (LPM) system. The results show that our approach significantly reduces the risk of becoming trapped in a local minimum.
  • Publication
    Pedestrian Detection and Tracking in Sparse Mls Point Clouds Using a Neural Network and Voting-Based Approach
    ( 2020)
    Borgmann, Björn
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    Hebel, Marcus orcid-logo
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    Arens, Michael
    ;
    Stilla, Uwe
    This paper presents and extends an approach for the detection of pedestrians in unstructured point clouds resulting from single MLS (mobile laser scanning) scans. The approach is based on a neural network and a subsequent voting process. The neural network processes point clouds subdivided into local point neighborhoods. The member points of these neighborhoods are directly processed by the network, hence a conversion in a structured representation of the data is not needed. The network also uses meta information of the neighborhoods themselves to improve the results, like their distance to the ground plane. It decides if the neighborhood is part of an object of interest and estimates the center of said object. This information is then used in a voting process. By searching for maxima in the voting space, the discrimination between an actual object and incorrectly classified neighborhoods is made. Since a single labeled object can be subdivided into multiple local neighborhoods, we are able to train the neural network with comparatively low amounts of labeled data. Considerations are made to deal with the varying and sparse point density that is typical for single MLS scans. We supplement the detection with a 3D tracking which, although straightforward, allows us to deal with objects which are occluded for short periods of time to improve the quality of the results. Overall, our approach performs reasonably well for the detection and tracking of pedestrians in single MLS scans as long as the local point density is not too low. Given the LiDAR sensor we used, this is the case up to distances of 22 m.
  • Publication
    Change Detection and Deformation Analysis based on Mobile Laser Scanning Data of Urban Areas
    ( 2020)
    Gehrung, Joachim
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    Hebel, Marcus orcid-logo
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    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 %.
  • 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
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    Gehrung, Joachim
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    Huang , Rong
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    Borgmann, Björn
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    Hoegner, Ludwig
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    Zhenghao, Sun
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    Hebel, Marcus orcid-logo
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    XU, Yusheng
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    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
    Error corrections for ultra-wideband ranging
    ( 2020)
    Sidorenko, Juri
    ;
    Schatz, Volker
    ;
    Scherer-Negenborn, Norbert
    ;
    Arens, Michael
    ;
    Hugentobler, Urs
    Precise indoor localization is a major challenge in the field of localization. In this work we investigate multiple error corrections for the ultra-wideband (UWB) technology, in particular the DecaWave DW1000 transceiver. Both the time-of-arrival (TOA) and the time-difference-of-arrival (TDOA) methods are considered. Various clock-drift correction methods for TOA from the literature are reviewed and compared experimentally. The best performing method is extended to TDOA, corrections for the signal power dependence and the hardware delay are added, and two additional enhancements suggested. These are compared to each other and to TOA in positioning experiments.
  • Publication
    Self-Calibration for the Time-of-Arrival Positioning
    ( 2020)
    Sidorenko, Juri
    ;
    Schatz, Volker
    ;
    Bulatov, Dimitri
    ;
    Scherer-Negenborn, Norbert
    ;
    Arens, Michael
    ;
    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.