Title: DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds,
Abstract: 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 properly defining unsupervised losses to "train" these DNNs through
back-propagation is equivalent to solving the underlying registration problem,
yet enables fewer dependencies on good initialization as required by 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 this http URL.