  This paper presents a novel 3D mapping robot with an omnidirectional
field-of-view (FoV) sensor suite composed of a non-repetitive LiDAR and an
omnidirectional camera. Thanks to the non-repetitive scanning nature of the
LiDAR, an automatic targetless co-calibration method is proposed to
simultaneously calibrate the intrinsic parameters for the omnidirectional
camera and the extrinsic parameters for the camera and LiDAR, which is crucial
for the required step in bringing color and texture information to the point
clouds in surveying and mapping tasks. Comparisons and analyses are made to
target-based intrinsic calibration and mutual information (MI)-based extrinsic
calibration, respectively. With this co-calibrated sensor suite, the hybrid
mapping robot integrates both the odometry-based mapping mode and stationary
mapping mode. Meanwhile, we proposed a new workflow to achieve coarse-to-fine
mapping, including efficient and coarse mapping in a global environment with
odometry-based mapping mode; planning for viewpoints in the region-of-interest
(ROI) based on the coarse map (relies on the previous work); navigating to each
viewpoint and performing finer and more precise stationary scanning and mapping
of the ROI. The fine map is stitched with the global coarse map, which provides
a more efficient and precise result than the conventional stationary approaches
and the emerging odometry-based approaches, respectively.
