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In many fields of robotics, knowing the relative position and orientationbetween two sensors is a mandatory precondition to operate with multiplesensing modalities. In this context, the pair LiDAR-RGB cameras offercomplementary features: LiDARs yield sparse high quality range measurements,while RGB cameras provide a dense color measurement of the environment.Existing techniques often rely either on complex calibration targets that areexpensive to obtain, or extracted virtual correspondences that can hinder theestimate's accuracy. In this paper we address the problem of LiDAR-RGBcalibration using typical calibration patterns (i.e. A3 chessboard) withminimal human intervention. Our approach exploits the planarity of the targetto find correspondences between the sensors measurements, leading to featuresthat are robust to LiDAR noise.  Moreover, we estimate a solution by solving a joint non-linear optimizationproblem. We validated our approach by carrying on quantitative and comparativeexperiments with other state-of-the-art approaches. Our results show that oursimple schema performs on par or better than other approches using complexcalibration targets. Finally, we release an open-source C++ implementation at\url{https://github.com/srrg-sapienza/ca2lib}

Ca$^2$Lib: Simple and Accurate LiDAR-RGB Calibration using Small Common Markers