  It is a significant problem to predict the 2D LiDAR map at next moment for
robotics navigation and path-planning. To tackle this problem, we resort to the
motion flow between adjacent maps, as motion flow is a powerful tool to process
and analyze the dynamic data, which is named optical flow in video processing.
However, unlike video, which contains abundant visual features in each frame, a
2D LiDAR map lacks distinctive local features. To alleviate this challenge, we
propose to estimate the motion flow based on deep neural networks inspired by
its powerful representation learning ability in estimating the optical flow of
the video. To this end, we design a recurrent neural network based on gated
recurrent unit, which is named LiDAR-FlowNet. As a recurrent neural network can
encode the temporal dynamic information, our LiDAR-FlowNet can estimate motion
flow between the current map and the unknown next map only from the current
frame and previous frames. A self-supervised strategy is further designed to
train the LiDAR-FlowNet model effectively, while no training data need to be
manually annotated. With the estimated motion flow, it is straightforward to
predict the 2D LiDAR map at the next moment. Experimental results verify the
effectiveness of our LiDAR-FlowNet as well as the proposed training strategy.
The results of the predicted LiDAR map also show the advantages of our motion
flow based method.
