  Global registration of point clouds aims to find an optimal alignment of a
sequence of 2D or 3D point sets. In this paper, we present a novel method that
takes advantage of current deep learning techniques for unsupervised learning
of global registration from a temporal sequence of point clouds. Our key
novelty is that we introduce a deep Spatio-Temporal REPresentation (STREP)
feature, which describes the geometric essence of both temporal and spatial
relationship of the sequence of point clouds acquired with sensors in an
unknown environment. In contrast to the previous practice that treats each time
step (pair-wise registration) individually, our unsupervised model starts with
optimizing a sequence of latent STREP feature, which is then decoded to a
temporally and spatially continuous sequence of geometric transformations to
globally align multiple point clouds. We have evaluated our proposed approach
over both simulated 2D and real 3D datasets and the experimental results
demonstrate that our method can beat other techniques by taking into account
the temporal information in deep feature learning.
