טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentElbaz Gil
Subject3D Point Cloud Registration for Localization Using a
Deep Neural Network Auto-Encoder
DepartmentDepartment of Mechanical Engineering
Supervisor Professor Anath Fischer
Full Thesis textFull thesis text - English Version


Abstract

Presented is 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 efficient 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.