  Recovering an outdoor environment's surface mesh is vital for an agricultural
robot during task planning and remote visualization. Our proposed solution is
based on a newly-designed panoramic stereo camera along with a hybrid novel
software framework that consists of three fusion modules. The panoramic stereo
camera with a pentagon shape consists of 5 stereo vision camera pairs to stream
synchronized panoramic stereo images for the following three fusion modules. In
the disparity fusion module, rectified stereo images produce the initial
disparity maps using multiple stereo vision algorithms. Then, these initial
disparity maps, along with the intensity images, are input into a disparity
fusion network to produce refined disparity maps. Next, the refined disparity
maps are converted into full-view point clouds or single-view point clouds for
the pose fusion module. The pose fusion module adopts a two-stage
global-coarse-to-local-fine strategy. In the first stage, each pair of
full-view point clouds is registered by a global point cloud matching algorithm
to estimate the transformation for a global pose graph's edge, which
effectively implements loop closure. In the second stage, a local point cloud
matching algorithm is used to match single-view point clouds in different
nodes. Next, we locally refine the poses of all corresponding edges in the
global pose graph using three proposed rules, thus constructing a refined pose
graph. The refined pose graph is optimized to produce a global pose trajectory
for volumetric fusion. In the volumetric fusion module, the global poses of all
the nodes are used to integrate the single-view point clouds into the volume to
produce the mesh of the whole garden. The proposed framework and its three
fusion modules are tested on a real outdoor garden dataset to show the
superiority of the performance.
