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    <pageFirst>857</pageFirst>
    <pageLast>864</pageLast>
    <pageNumber>8</pageNumber>
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    <title language="eng">Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment</title>
    <abstract language="eng">To arrive at a realistic assessment of localization methods in terms of their performance in an industrial environment under various challenging conditions, we provide a benchmark to evaluate algorithms both for individual components as well as multi-sensor systems. For several sensor types, including wheel odometry, RGB cameras, RGB-D cameras, and LIDAR, potential issues were identified. The accuracy of wheel odometry, for example, when there are bumps on the track. For each sensor type, we explicitly chose a track for the benchmark dataset containing situations where the sensor fails to provide adequate measurements. Based on the acquired sensor data, localization can be achieved either using a single sensor information or sensor fusion. To help evaluate the output of associated localization algorithms, we provide a software to evaluate a set of metrics as part of the paper. An example application of the benchmark with state-of-the-art algorithms for each sensor is also provided.</abstract>
    <parentTitle language="eng">2021 20th International Conference on Advanced Robotics (ICAR)</parentTitle>
    <identifier type="doi">10.1109/ICAR53236.2021.9659355</identifier>
    <enrichment key="opus.import.data">@inproceedingsspiess2021towards, title=Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment, author=Spieß, Florian and Friesslich, Jonas and Bluemm, Daniel and Mast, Fabio and Vinokour, Dmitrij and Kounev, Samuel and Kaupp, Tobias and Strobel, Norbert, booktitle=2021 20th International Conference on Advanced Robotics (ICAR), pages=857–864, year=2021, organization=IEEE</enrichment>
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    <author>Florian Spieß</author>
    <author>Jonas Friesslich</author>
    <author>Daniel Bluemm</author>
    <author>Fabio Mast</author>
    <author>Dmitrij Vinokour</author>
    <author>Samuel Kounev</author>
    <author>Tobias Kaupp</author>
    <author>Norbert Strobel</author>
    <collection role="institutes" number="fe">Fakultät Elektrotechnik</collection>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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  <doc>
    <id>3258</id>
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    <publishedYear>2021</publishedYear>
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    <language>eng</language>
    <pageFirst>135</pageFirst>
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    <issue>4</issue>
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    <title language="eng">People detection with depth silhouettes and convolutional neural networks on a mobile robot</title>
    <abstract language="eng">This paper presents a novel people detection approach for mobile robot applications based on a combination of classical computer vision techniques and a state-of-the-art neural network. Our approach involves an RGB-D camera as an environmental sensor. The depth data is used to extract silhouettes around people. The RGB images are subsequently augmented with this border information before passing it to the neural network. Under challenging lighting conditions, our system was able to outperform the neural network trained on regular RGB data alone by a factor of two.</abstract>
    <parentTitle language="eng">Journal of Image and Graphics</parentTitle>
    <identifier type="url">http://www.joig.net/index.php?m=content&amp;c=index&amp;a=show&amp;catid=72&amp;id=277</identifier>
    <enrichment key="opus.import.data">@articlespiess2021people, title=People detection with depth silhouettes and convolutional neural networks on a mobile robot, author=Spiess, Florian and Reinhart, Lucas and Strobel, Norbert and Kaiser, Dennis and Kounev, Samuel and Kaupp, Tobias, journal=Journal of Image and Graphics, volume=9, number=4, pages=135–139, year=2021</enrichment>
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    <author>Florian Spiess</author>
    <author>Lukas Reinhart</author>
    <author>Norbert Strobel</author>
    <author>Tobias Kaupp</author>
    <author>Dennis Kaiser</author>
    <author>Samuel Kounev</author>
    <collection role="institutes" number="fe">Fakultät Elektrotechnik</collection>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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  <doc>
    <id>3274</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
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    <language>eng</language>
    <pageFirst>76</pageFirst>
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    <title language="eng">A data-driven Sensor Model for LIDAR Range Measurements used for Mobile Robot Navigation</title>
    <abstract language="eng">In this paper, an analysis of the precision of LIDAR range measurements is presented. LIDAR data from two different sensors (HLS-LFCD-LDS and SICK TIM561) were analyzed regarding the influence of range, incident angle to the surface, and material. Based on the results, a data-driven model for LIDAR precision behavior was developed, and a comparison with standard deviation models based on the vendor-provided specifications was presented. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.</abstract>
    <parentTitle language="eng">2022 Sixth IEEE International Conference on Robotic Computing (IRC)</parentTitle>
    <identifier type="doi">10.1109/IRC55401.2022.00020</identifier>
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    <author>Florian Spiess</author>
    <author>Norbert Strobel</author>
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    <author>Samuel Kounev</author>
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    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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    <id>3235</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
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    <language>eng</language>
    <pageFirst>1532</pageFirst>
    <pageLast>1540</pageLast>
    <pageNumber>9</pageNumber>
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    <issue>12</issue>
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    <title language="eng">Survey and Experimental Comparison of RGB-D Indoor Robot Navigation Methods Supported by ROS and Their Expansion via Fusion with Wheel Odometry and IMU Data</title>
    <parentTitle language="eng">International Journal of Mechanical Engineering and Robotics Research</parentTitle>
    <identifier type="doi">10.18178/ijmerr.9.12.1532-1540</identifier>
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    <author>Florian Spiess</author>
    <author>Jonas Friesslich</author>
    <author>Tobias Kaupp</author>
    <author>Samuel Kounev</author>
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    <collection role="institutes" number="fe">Fakultät Elektrotechnik</collection>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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    <id>3275</id>
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    <publishedYear>2023</publishedYear>
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    <title language="eng">A data-driven Model for Range Sensors</title>
    <parentTitle language="eng">Encyclopedia with Semantic Computing and Robotic Intelligence</parentTitle>
    <identifier type="url">https://www.worldscientific.com/doi/abs/10.1142/S2529737623500065?journalCode=escri</identifier>
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    <author>Florian Spiess</author>
    <author>Norbert Strobel</author>
    <author>Tobias Kaupp</author>
    <author>Samuel Kounev</author>
    <collection role="institutes" number="fe">Fakultät Elektrotechnik</collection>
    <collection role="Autoren" number="kaupp">Tobias Kaupp</collection>
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