  We propose DeepMapping, a novel registration framework using deep neural
networks (DNNs) as auxiliary functions to align multiple point clouds from
scratch to a globally consistent frame. We use DNNs to model the highly
non-convex mapping process that traditionally involves hand-crafted data
association, sensor pose initialization, and global refinement. Our key novelty
is that "training" these DNNs with properly defined unsupervised losses is
equivalent to solving the underlying registration problem, but less sensitive
to good initialization than ICP. Our framework contains two DNNs: a
localization network that estimates the poses for input point clouds, and a map
network that models the scene structure by estimating the occupancy status of
global coordinates. This allows us to convert the registration problem to a
binary occupancy classification, which can be solved efficiently using
gradient-based optimization. We further show that DeepMapping can be readily
extended to address the problem of Lidar SLAM by imposing geometric constraints
between consecutive point clouds. Experiments are conducted on both simulated
and real datasets. Qualitative and quantitative comparisons demonstrate that
DeepMapping often enables more robust and accurate global registration of
multiple point clouds than existing techniques. Our code is available at
https://ai4ce.github.io/DeepMapping/.
