  In this work, we present a novel target-based lidar-camera extrinsic
calibration methodology that can be used for non-overlapping field of view
(FOV) sensors. Contrary to previous work, our methodology overcomes the
non-overlapping FOV challenge using a motion capture system (MCS) instead of
traditional simultaneous localization and mapping approaches. Due to the high
relative precision of the MCS, our methodology can achieve both the high
accuracy and repeatable calibrations of traditional target-based methods,
regardless of the amount of overlap in the field of view of the sensors. We
show using simulation that we can accurately recover extrinsic calibrations for
a range of perturbations to the true calibration that would be expected in real
circumstances. We also validate that high accuracy calibrations can be achieved
on experimental data. Furthermore, We implement the described approach in an
extensible way that allows any camera model, target shape, or feature
extraction methodology to be used within our framework. We validate this
implementation on two target shapes: an easy to construct cylinder target and a
diamond target with a checkerboard. The cylinder target shape results show that
our methodology can be used for degenerate target shapes where target poses
cannot be fully constrained from a single observation, and distinct repeatable
features need not be detected on the target.
