  The demand for multimodal sensing systems for robotics is growing due to the
increase in robustness, reliability and accuracy offered by these systems.
These systems also need to be spatially and temporally co-registered to be
effective. In this paper, we propose a targetless and structureless
spatiotemporal camera-LiDAR calibration method. Our method combines a
closed-form solution with a modified structureless bundle adjustment where the
coarse-to-fine approach does not {require} an initial guess on the
spatiotemporal parameters. Also, as 3D features (structure) are calculated from
triangulation only, there is no need to have a calibration target or to match
2D features with the 3D point cloud which provides flexibility in the
calibration process and sensor configuration. We demonstrate the accuracy and
robustness of the proposed method through both simulation and real data
experiments using multiple sensor payload configurations mounted to hand-held,
aerial and legged robot systems. Also, qualitative results are given in the
form of a colorized point cloud visualization.
