Scipediacontent: Scipediacontent moved page Draft Content 873794019 to Muller et al 2019a

2021-02-03T16:29:48Z

Scipediacontent moved page Draft Content 873794019 to Muller et al 2019a

Scipediacontent: Created page with " == Abstract == Sensor calibration usually is a time consuming yet important task. While classical approaches are sensor-specific and often need calibration targets as well a..."

2021-02-03T16:29:40Z

Created page with " == Abstract == Sensor calibration usually is a time consuming yet important task. While classical approaches are sensor-specific and often need calibration targets as well a..."

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== Abstract ==

Sensor calibration usually is a time consuming yet important task. While classical approaches are sensor-specific and often need calibration targets as well as a widely overlapping field of view (FOV), within this work, a cooperative intelligent vehicle is used as callibration target. The vehicleis detected in the sensor frame and then matched with the information received from the cooperative awareness messagessend by the coperative intelligent vehicle. The presented algorithm is fully automated as well as sensor-independent, relying only on a very common set of assumptions. Due to the direct registration on the world frame, no overlapping FOV is necessary. The algorithm is evaluated through experiment for four laserscanners as well as one pair of stereo cameras showing a repetition error within the measurement uncertainty of the sensors. A plausibility check rules out systematic errors that might not have been covered by evaluating the repetition error.

Comment: 6 pages, published at ITSC 2019

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1911.01711 http://arxiv.org/abs/1911.01711]

* [http://arxiv.org/pdf/1911.01711 http://arxiv.org/pdf/1911.01711]

* [http://dx.doi.org/10.1109/itsc.2019.8917310 http://dx.doi.org/10.1109/itsc.2019.8917310]

* [http://xplorestaging.ieee.org/ielx7/8907344/8916833/08917310.pdf?arnumber=8917310 http://xplorestaging.ieee.org/ielx7/8907344/8916833/08917310.pdf?arnumber=8917310],
: [http://dx.doi.org/10.1109/itsc.2019.8917310 http://dx.doi.org/10.1109/itsc.2019.8917310]

* [https://dblp.uni-trier.de/db/journals/corr/corr1911.html#abs-1911-01711 https://dblp.uni-trier.de/db/journals/corr/corr1911.html#abs-1911-01711],
: [https://arxiv.org/abs/1911.01711 https://arxiv.org/abs/1911.01711],
: [https://ui.adsabs.harvard.edu/abs/2019arXiv191101711M/abstract https://ui.adsabs.harvard.edu/abs/2019arXiv191101711M/abstract],
: [http://arxiv.org/pdf/1911.01711.pdf http://arxiv.org/pdf/1911.01711.pdf],
: [https://academic.microsoft.com/#/detail/2990557209 https://academic.microsoft.com/#/detail/2990557209]

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Muller et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 873794019 to Muller et al 2019a

Scipediacontent: Created page with " == Abstract == Sensor calibration usually is a time consuming yet important task. While classical approaches are sensor-specific and often need calibration targets as well a..."