  Ground robots always get collision in that only if they get close to the
obstacles, can they sense the danger and take actions, which is usually too
late to avoid the crash, causing severe damage to the robots. To address this
issue, we present collaboration of aerial and ground robots in recognition of
feasible region. Taking the aerial robots' advantages of having large scale
variance of view points of the same route which the ground robots is on, the
collaboration work provides global information of road segmentation for the
ground robot, thus enabling it to obtain feasible region and adjust its pose
ahead of time. Under normal circumstance, the transformation between these two
devices can be obtained by GPS yet with much error, directly causing inferior
influence on recognition of feasible region. Thereby, we utilize the
state-of-the-art research achievements in matching heterogeneous sensor
measurements called deep phase correlation network(DPCN), which has excellent
performance on heterogeneous mapping, to refine the transformation. The network
is light-weighted and promising for better generalization. We use Aero-Ground
dataset which consists of heterogeneous sensor images and aerial road
segmentation images. The results show that our collaborative system has great
accuracy, speed and stability.
