  Most sensor setups for onboard autonomous perception are composed of LiDARs
and vision systems, as they provide complementary information that improves the
reliability of the different algorithms necessary to obtain a robust scene
understanding. However, the effective use of information from different sources
requires an accurate calibration between the sensors involved, which usually
implies a tedious and burdensome process. We present a method to calibrate the
extrinsic parameters of any pair of sensors involving LiDARs, monocular or
stereo cameras, of the same or different modalities. The procedure is composed
of two stages: first, reference points belonging to a custom calibration target
are extracted from the data provided by the sensors to be calibrated, and
second, the optimal rigid transformation is found through the registration of
both point sets. The proposed approach can handle devices with very different
resolutions and poses, as usually found in vehicle setups. In order to assess
the performance of the proposed method, a novel evaluation suite built on top
of a popular simulation framework is introduced. Experiments on the synthetic
environment show that our calibration algorithm significantly outperforms
existing methods, whereas real data tests corroborate the results obtained in
the evaluation suite. Open-source code is available at
https://github.com/beltransen/velo2cam_calibration
