Publications

A Data-Driven Model for Range Sensors. Spiess, Florian; Strobel, Norbert; Kaupp, Tobias; Kounev, Samuel; in International Journal of Semantic Computing (2024). 1–18.

[ Abstract ]
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In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.

@article{doi:10.1142/S1793351X24300012,
  abstract = {In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.},
  author = {Spiess, Florian and Strobel, Norbert and Kaupp, Tobias and Kounev, Samuel},
  journal = {International Journal of Semantic Computing},
  keywords = {t_journalmagazine},
  month = {02},
  pages = {1-18},
  title = {A Data-Driven Model for Range Sensors},
  year = 2024
}

%0 Journal Article
%1 doi:10.1142/S1793351X24300012
%A Spiess, Florian
%A Strobel, Norbert
%A Kaupp, Tobias
%A Kounev, Samuel
%D 2024
%J International Journal of Semantic Computing
%P 1-18
%T A Data-Driven Model for Range Sensors
%U https://doi.org/10.1142/S1793351X24300012
%X In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.

BLOCSIE - Benchmark for LOCalization in a Simulated Industrial Environment. Spiess, Florian; Bluemm, Daniel; Strobel, Norbert; Kaupp, Tobias; Kounev, Samuel; in 2023 21st International Conference on Advanced Robotics (ICAR) (2023). 577–584.

[ BibTeX ]
[ EndNote ]

@inproceedings{10406777,
  author = {Spiess, Florian and Bluemm, Daniel and Strobel, Norbert and Kaupp, Tobias and Kounev, Samuel},
  booktitle = {2023 21st International Conference on Advanced Robotics (ICAR)},
  keywords = {descartes},
  pages = {577-584},
  title = {BLOCSIE - Benchmark for LOCalization in a Simulated Industrial Environment},
  year = 2023
}

%0 Conference Paper
%1 10406777
%A Spiess, Florian
%A Bluemm, Daniel
%A Strobel, Norbert
%A Kaupp, Tobias
%A Kounev, Samuel
%B 2023 21st International Conference on Advanced Robotics (ICAR)
%D 2023
%P 577-584
%T BLOCSIE - Benchmark for LOCalization in a Simulated Industrial Environment

A data-driven Sensor Model for LIDAR Range Measurements used for Mobile Robot Navigation. Spiess, Florian; Strobel, Norbert; Kaupp, Tobias; Kounev, Samuel; in Proceedings of the IEEE IRC 2022 Conference (2022).

[ BibTeX ]
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@conference{spiess2022datadriven,
  author = {Spiess, Florian and Strobel, Norbert and Kaupp, Tobias and Kounev, Samuel},
  booktitle = {Proceedings of the IEEE IRC 2022 Conference},
  keywords = {t_interdisciplinary},
  title = {A data-driven Sensor Model for LIDAR Range Measurements used for Mobile Robot Navigation},
  year = 2022
}

%0 Generic
%1 spiess2022datadriven
%A Spiess, Florian
%A Strobel, Norbert
%A Kaupp, Tobias
%A Kounev, Samuel
%B Proceedings of the IEEE IRC 2022 Conference
%D 2022
%T A data-driven Sensor Model for LIDAR Range Measurements used for Mobile Robot Navigation

Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment. Spiess, Florian; Friesslich, Jonas; Bluemm, Daniel; Mast, Fabio; Vinokour, Dmitrij; Kounev, Samuel; Kaupp, Tobias; Strobel, Norbert; in 2021 20th International Conference on Advanced Robotics (ICAR) (2021). 857–864.

[ Abstract ]
[ BibTeX ]
[ EndNote ]
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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.

@inproceedings{9659355,
  abstract = {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.},
  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)},
  keywords = {t_interdisciplinary},
  month = 12,
  pages = {857-864},
  title = {Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment},
  year = 2021
}

%0 Conference Paper
%1 9659355
%A Spiess, Florian
%A Friesslich, Jonas
%A Bluemm, Daniel
%A Mast, Fabio
%A Vinokour, Dmitrij
%A Kounev, Samuel
%A Kaupp, Tobias
%A Strobel, Norbert
%B 2021 20th International Conference on Advanced Robotics (ICAR)
%D 2021
%P 857-864
%T Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment
%U https://ieeexplore.ieee.org/document/9659355/
%X 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.

People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot. Spiess, Florian; Reinhart, Lucas; Strobel, Norbert; Kaiser, Dennis; Kounev, Samuel; Kaupp, Tobias; in Journal of Image and Graphics, (J. of Image; Graphics, Hrsg.) (2021). 9(4) 135–139.

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@article{spiess2021people,
  author = {Spiess, Florian and Reinhart, Lucas and Strobel, Norbert and Kaiser, Dennis and Kounev, Samuel and Kaupp, Tobias},
  editor = {of Image, Journal and Graphics},
  journal = {Journal of Image and Graphics},
  keywords = {sys:relevantfor:se-group},
  month = 12,
  number = 4,
  pages = {135-139},
  title = {People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot},
  volume = 9,
  year = 2021
}

%0 Journal Article
%1 spiess2021people
%A Spiess, Florian
%A Reinhart, Lucas
%A Strobel, Norbert
%A Kaiser, Dennis
%A Kounev, Samuel
%A Kaupp, Tobias
%D 2021
%E of Image, Journal
%E Graphics, 
%J Journal of Image and Graphics
%N 4
%P 135-139
%T People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot
%U /brokenurl# http://www.joig.net/index.php?m=content&c=index&a=show&catid=72&id=277
%V 9

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. Spiess, Florian; Friesslich, Jonas; Tobias, Kaupp; Kounev, Samuel; Strobel, Norbert; in International Journal of Mechanical Engineering and Robotics Research (IJMERR), (P. R. (Chunhui) Yang, Hrsg.) (2020). 9(12) 1532–1540.

[ Abstract ]
[ BibTeX ]
[ EndNote ]
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This paper presents an experimental evaluation and comparison of selected Visual Odometry (VO) and Visual-SLAM (V-SLAM) algorithms for indoor mobile robot navigation supported by the Robot Operating System (ROS). The focus is on algorithms involving RGB-D cameras. Since RGB-D cameras integrate color and depth information, they output coherent measurement data and facilitate an efficient processing pipeline. The various underlying methods of vision-based algorithms are described and evaluated on two datasets covering different indoor situations as well as various lighting and movement conditions. In general, V-SLAM algorithms yielded better results. They were superior with respect to handling drift, in particular when loop closures were involved. However, the results confirmed that VO algorithms could outperform V-SLAM methods under certain circumstances. This happened when there was a very good match between an algorithm’s design objectives and the situation at hand. While the experiments showed that there is no single best algorithm for every scenario, ORB-SLAM2 is recommended as a robust stand-alone RGB-D based localization method available under ROS. Furthermore, we observed that the position estimation error could be reduced by around 67% on average when combining vision-based position estimates with sensor data obtained from wheel odometry and an inertial measurement unit (IMU), respectively. This clearly demonstrates the potential of sensor fusion techniques. The best results in case of sensor fusion were obtained with RGB-DSLAMv2.

@article{yang2020survey,
  abstract = {This paper presents an experimental evaluation and comparison of selected Visual Odometry (VO) and Visual-SLAM (V-SLAM) algorithms for indoor mobile robot navigation supported by the Robot Operating System (ROS). The focus is on algorithms involving RGB-D cameras. Since RGB-D cameras integrate color and depth information, they output coherent measurement data and facilitate an efficient processing pipeline. The various underlying methods of vision-based algorithms are described and evaluated on two datasets covering different indoor situations as well as various lighting and movement conditions. In general, V-SLAM algorithms yielded better results. They were superior with respect to handling drift, in particular when loop closures were involved. However, the results confirmed that VO algorithms could outperform V-SLAM methods under certain circumstances. This happened when there was a very good match between an algorithm’s design objectives and the situation at hand. While the experiments showed that there is no single best algorithm for every scenario, ORB-SLAM2 is recommended as a robust stand-alone RGB-D based localization method available under ROS. Furthermore, we observed that the position estimation error could be reduced by around 67% on average when combining vision-based position estimates with sensor data obtained from wheel odometry and an inertial measurement unit (IMU), respectively. This clearly demonstrates the potential of sensor fusion techniques. The best results in case of sensor fusion were obtained with RGB-DSLAMv2.},
  author = {Spiess, Florian and Friesslich, Jonas and Tobias, Kaupp and Kounev, Samuel and Strobel, Norbert},
  editor = {Yang, Prof Richard (Chunhui)},
  journal = {International Journal of Mechanical Engineering and Robotics Research (IJMERR)},
  keywords = {rgb},
  month = 12,
  number = 12,
  pages = {1532-1540},
  title = {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},
  volume = 9,
  year = 2020
}

%0 Journal Article
%1 yang2020survey
%A Spiess, Florian
%A Friesslich, Jonas
%A Tobias, Kaupp
%A Kounev, Samuel
%A Strobel, Norbert
%D 2020
%E Yang, Prof Richard (Chunhui)
%J International Journal of Mechanical Engineering and Robotics Research (IJMERR)
%N 12
%P 1532-1540
%T 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
%U http://www.ijmerr.com/index.php?m=content&c=index&a=show&catid=185&id=1529
%V 9
%X This paper presents an experimental evaluation and comparison of selected Visual Odometry (VO) and Visual-SLAM (V-SLAM) algorithms for indoor mobile robot navigation supported by the Robot Operating System (ROS). The focus is on algorithms involving RGB-D cameras. Since RGB-D cameras integrate color and depth information, they output coherent measurement data and facilitate an efficient processing pipeline. The various underlying methods of vision-based algorithms are described and evaluated on two datasets covering different indoor situations as well as various lighting and movement conditions. In general, V-SLAM algorithms yielded better results. They were superior with respect to handling drift, in particular when loop closures were involved. However, the results confirmed that VO algorithms could outperform V-SLAM methods under certain circumstances. This happened when there was a very good match between an algorithm’s design objectives and the situation at hand. While the experiments showed that there is no single best algorithm for every scenario, ORB-SLAM2 is recommended as a robust stand-alone RGB-D based localization method available under ROS. Furthermore, we observed that the position estimation error could be reduced by around 67% on average when combining vision-based position estimates with sensor data obtained from wheel odometry and an inertial measurement unit (IMU), respectively. This clearly demonstrates the potential of sensor fusion techniques. The best results in case of sensor fusion were obtained with RGB-DSLAMv2.

{Towards Self-Aware Multirotor Formations}. Kaiser, Dennis; Lesch, Veronika; Rothe, Julian; Strohmeier, Michael; Spiess, Florian; Krupitzer, Christian; Montenegro, Sergio; Kounev, Samuel; in Computers (2020). 9(7)

Special Issue on Self-Aware Computing

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@article{Kaiser2020Towards,
  author = {Kaiser, Dennis and Lesch, Veronika and Rothe, Julian and Strohmeier, Michael and Spiess, Florian and Krupitzer, Christian and Montenegro, Sergio and Kounev, Samuel},
  journal = {Computers},
  keywords = {Intelligent_Transportation_Systems},
  month = {02},
  note = {Special Issue on Self-Aware Computing},
  number = 7,
  publisher = {MDPI},
  title = {{Towards Self-Aware Multirotor Formations}},
  volume = 9,
  year = 2020
}

%0 Journal Article
%1 Kaiser2020Towards
%A Kaiser, Dennis
%A Lesch, Veronika
%A Rothe, Julian
%A Strohmeier, Michael
%A Spiess, Florian
%A Krupitzer, Christian
%A Montenegro, Sergio
%A Kounev, Samuel
%D 2020
%I MDPI
%J Computers
%N 7
%T {Towards Self-Aware Multirotor Formations}
%V 9

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