Global ray-casting range image registration

Linh Tao,Tam Bui,Hiroshi Hasegawa

doi:10.1186/s41074-017-0025-4

Abstract

This paper presents a novel method for pair-wise range image registration, a backbone task in world modeling, parts inspection and manufacture, object recognition, pose estimation, robotic navigation, and reverse engineering. The method finds the most suitable homogeneous transformation matrix between two constructed range images to create a more complete 3D view of a scene. The proposed solution integrates a ray casting-based fitness estimation with a global optimization method called improved self-adaptive differential evolution. This method eliminates the fine registration steps of the well-known iterative closest point (ICP) algorithm used in previously proposed methods, and thus, is the first direct global registration algorithm. With its parallel implementation potential, the ray casting-based algorithm speeds up the fitness calculation for the global optimization method, which effectively exploits the search space to find the best transformation solution. The integration was successfully implemented in a parallel paradigm on a multi-core computer processor to solve a simultaneous 3D localization problem. The fast, accurate, and robust results show that the proposed algorithm significantly improves on the registration problem over state-of-the-art algorithms.

Highlights

The introduction of commercial depth sensing devices, such as the Microsoft Kinect and Asus Xtion, has shifted the research areas of robotics and computer vision from 2D-based imaging and laser scanning toward 3D-based depth scenes for environment processing
The approach of using EAs, in particular in new methods, proved their potential for tackling the image registration problem based on their robustness and accuracy for searching for global optimal solutions
We proposed a novel registration method in which a fast ray-casting-based error calculation is integrated with a powerful self-adaptive optimization algorithm

Summary

Introduction

The introduction of commercial depth sensing devices, such as the Microsoft Kinect and Asus Xtion, has shifted the research areas of robotics and computer vision from 2D-based imaging and laser scanning toward 3D-based depth scenes for environment processing. To overcome the shortage of ICP-class methods, automatic registration algorithms in general perform two steps: coarse initialization and fine transformation. Two approaches for coarse transformation, pre-alignment estimation, or initialization exist: local and global. The former uses local descriptors (or signatures), such as PFH [7] and SIFT [8], which encode local shape variation in neighborhood points. By virtue of new search algorithms, in particular heuristic optimal methods, and the increase in computer speed achieved by using multi-core computer processor units (CPUs) and graphic computation units (GPUs) [12], it is possible to find reasonable solutions using global approaches for the registration problem. A new global direct registration method for 3D constructed surfaces captured by range cameras in cases where the initialization is not good is proposed. - Section 1 comprises the introduction. - In Section 2, the classic and up-to-date methods of range image registration are presented. - In Section 3, the methodology and the new approach of the proposed method are provided. - In Section 4, the experiments and results are described. - In Section 5, the discussion and conclusions are presented

Registration error function and ICP approach

Parameter settings

Comparison between different optimization algorithms

Results from registering in different movement patterns and frame distances

Discussion