  Point cloud based place recognition is still an open issue due to the
difficulty in extracting local features from the raw 3D point cloud and
generating the global descriptor, and it's even harder in the large-scale
dynamic environments. In this paper, we develop a novel deep neural network,
named LPD-Net (Large-scale Place Description Network), which can extract
discriminative and generalizable global descriptors from the raw 3D point
cloud. Two modules, the adaptive local feature extraction module and the
graph-based neighborhood aggregation module, are proposed, which contribute to
extract the local structures and reveal the spatial distribution of local
features in the large-scale point cloud, with an end-to-end manner. We
implement the proposed global descriptor in solving point cloud based retrieval
tasks to achieve the large-scale place recognition. Comparison results show
that our LPD-Net is much better than PointNetVLAD and reaches the
state-of-the-art. We also compare our LPD-Net with the vision-based solutions
to show the robustness of our approach to different weather and light
conditions.
