@inproceedings{meyer_automatic_2021,
	address = {UT Austin, USA},
	title = {Automatic {Extrinsic} {Rotational} {Calibration} of {LiDAR} {Sensors} and {Vehicle} {Orientation} {Estimation}},
	abstract = {Whether a vehicle is being used for an autonomous mission or in support of data collection and
research, extrinsic calibration to align the vehicle’s sensors to a reference point on the vehicle is integral
to ensuring that quality data is available to the system. This is particularly true for vision and distance
sensors, such as LiDAR, which must be well-located with respect to the vehicle body before they can
provide meaningful localization or environmental modeling assistance. This paper outlines an automatic
approach for calibrating a LiDAR to the body of a ground vehicle using only the data from the LiDAR
itself. This approach assumes that the LiDAR is able to sample at least a one meter square section of
level ground, and that the vehicle travels along a straight section of roadway with a well-marked road
edge at some point during the calibration, while closely following the trajectory of the road edge. This
method has been able to automatically calibrate a LiDAR to yield 0.08 and 0.12 degrees of error in roll
and pitch respectively when comparing estimated ground pitch and roll to the orientation estimates from
a truth sensor.},
	booktitle = {Advanced {Signal} {Processing}, {Estimation} and {Control} in {Automotive} {Applications}},
	author = {Meyer, Stephanie and Chen, Howard and Bevly, David},
	month = oct,
	year = {2021},
}

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