3D Point Cloud Registration for Localization using a
Deep Neural Network Auto-Encoder
Abstract
We present an algorithm for registration between a
large-scale point cloud and a close-proximity scanned point
cloud, providing a localization solution that is fully independent of prior information about the initial positions
of the two point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points—local subsets
of points—and describes the geometric structure of each
with a low-dimensional descriptor. These descriptors are
then used to infer potential matching regions for an effi-
cient coarse registration process, followed by a fine-tuning
stage. The set of super-points is selected by covering the
point clouds with overlapping spheres, and then filtering out
those of low-quality or nonsalient regions. The descriptors
are computed using state-of-the-art unsupervised machine
learning, utilizing the technology of deep neural network
based auto-encoders.
This novel framework provides a strong alternative to
the common practice of using manually designed key-point
descriptors for coarse point cloud registration. Utilizing
super-points instead of key-points allows the available geometrical data to be better exploited to find the correct transformation. Encoding local 3D geometric structures using
a deep neural network auto-encoder instead of traditional
descriptors continues the trend seen in other computer vision applications and indeed leads to superior results. The
algorithm is tested on challenging point cloud registration
datasets, and its advantages over previous approaches as
well as its robustness to density changes, noise and missing
data are shown