  In this paper, we present RegNet, the first deep convolutional neural network
(CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between
multimodal sensors, exemplified using a scanning LiDAR and a monocular camera.
Compared to existing approaches, RegNet casts all three conventional
calibration steps (feature extraction, feature matching and global regression)
into a single real-time capable CNN. Our method does not require any human
interaction and bridges the gap between classical offline and target-less
online calibration approaches as it provides both a stable initial estimation
as well as a continuous online correction of the extrinsic parameters. During
training we randomly decalibrate our system in order to train RegNet to infer
the correspondence between projected depth measurements and RGB image and
finally regress the extrinsic calibration. Additionally, with an iterative
execution of multiple CNNs, that are trained on different magnitudes of
decalibration, our approach compares favorably to state-of-the-art methods in
terms of a mean calibration error of 0.28 degrees for the rotational and 6 cm
for the translation components even for large decalibrations up to 1.5 m and 20
degrees.
