  This paper designs a technique route to generate high-quality panoramic image
with depth information, which involves two critical research hotspots: fusion
of LiDAR and image data and image stitching. For the fusion of 3D points and
image data, since a sparse depth map can be firstly generated by projecting
LiDAR point onto the RGB image plane based on our reliable calibrated and
synchronized sensors, we adopt a parameter self-adaptive framework to produce
2D dense depth map. For image stitching, optimal seamline for the overlapping
area is searched using a graph-cuts-based method to alleviate the geometric
influence and image blending based on the pyramid multi-band is utilized to
eliminate the photometric effects near the stitching line. Since each pixel is
associated with a depth value, we design this depth value as a radius in the
spherical projection which can further project the panoramic image to the world
coordinate and consequently produces a high-quality measurable panoramic image.
The purposed method is tested on the data from our data collection platform and
presents a satisfactory application prospects.
