  The fusion of multi-modal sensors has become increasingly popular in
autonomous driving and intelligent robots since it can provide richer
information than any single sensor, enhance reliability in complex
environments. Multi-sensor extrinsic calibration is one of the key factors of
sensor fusion. However, such calibration is difficult due to the variety of
sensor modalities and the requirement of calibration targets and human labor.
In this paper, we demonstrate a new targetless cross-modal calibration
framework by focusing on the extrinsic transformations among stereo cameras,
thermal cameras, and laser sensors. Specifically, the calibration between
stereo and laser is conducted in 3D space by minimizing the registration error,
while the thermal extrinsic to the other two sensors is estimated by optimizing
the alignment of the edge features. Our method requires no dedicated targets
and performs the multi-sensor calibration in a single shot without human
interaction. Experimental results show that the calibration framework is
accurate and applicable in general scenes.
