  We propose a robust calibration pipeline that optimises the selection of
calibration samples for the estimation of calibration parameters that fit the
entire scene. We minimise user error by automating the data selection process
according to a metric, called Variability of Quality (VOQ) that gives a score
to each calibration set of samples. We show that this VOQ score is correlated
with the estimated calibration parameter's ability to generalise well to the
entire scene, thereby overcoming the overfitting problems of existing
calibration algorithms. Our approach has the benefits of simplifying the
calibration process for practitioners of any calibration expertise level and
providing an objective measure of the quality for our calibration pipeline's
input and output data. We additionally use a novel method of assessing the
accuracy of the calibration parameters. It involves computing reprojection
errors for the entire scene to ensure that the parameters are well fitted to
all features in the scene. Our proposed calibration pipeline takes 90s, and
obtains an average reprojection error of 1-1.2cm, with standard deviation of
0.4-0.5cm over 46 poses evenly distributed in a scene. This process has been
validated by experimentation on a high resolution, software definable lidar,
Baraja Spectrum-Scan; and a low, fixed resolution lidar, Velodyne VLP-16. We
have shown that despite the vast differences in lidar technologies, our
proposed approach manages to estimate robust calibration parameters for both.
Our code and data set used for this paper are made available as open-source.
