Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment

Spiess F, Friesslich J, Bluemm D, Mast F, Vinokour D, Kounev S, Kaupp T, Strobel N (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: IEEE

City/Town: NEW YORK

Pages Range: 857-864

Conference Proceedings Title: 2021 20TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR)

DOI: 10.1109/ICAR53236.2021.9659355

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.

Involved external institutions

Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt DE Germany (DE) Julius-Maximilians-Universität Würzburg DE Germany (DE)

How to cite

APA:

Spiess, F., Friesslich, J., Bluemm, D., Mast, F., Vinokour, D., Kounev, S.,... Strobel, N. (2021). Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment. In 2021 20TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR) (pp. 857-864). NEW YORK: IEEE.

MLA:

Spiess, Florian, et al. "Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment." Proceedings of the 20th International Conference on Advanced Robotics (ICAR) NEW YORK: IEEE, 2021. 857-864.

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