%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 %K descartes t_full t_interdisciplinary myown %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.

@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.}, added-at = {2022-02-09T10:45:15.000+0100}, author = {Spiess, Florian and Friesslich, Jonas and Bluemm, Daniel and Mast, Fabio and Vinokour, Dmitrij and Kounev, Samuel and Kaupp, Tobias and Strobel, Norbert}, biburl = {https://www.bibsonomy.org/bibtex/2686c4c6b207d3dc5fb902050e07ee625/florian.spiess}, booktitle = {2021 20th International Conference on Advanced Robotics (ICAR)}, interhash = {9370817d1b45ab7e89b84b808ddbd3ad}, intrahash = {686c4c6b207d3dc5fb902050e07ee625}, keywords = {descartes t_full t_interdisciplinary myown}, month = dec, pages = {857-864}, timestamp = {2022-11-24T00:05:04.000+0100}, title = {Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment}, url = {https://ieeexplore.ieee.org/document/9659355/}, year = 2021 }