Efficient 3D Object Recognition from Cluttered Point Cloud.

Wei Li,Hongtai Cheng,Xiaohua Zhang

doi:10.3390/s21175850

Abstract

Recognizing 3D objects and estimating their postures in a complex scene is a challenging task. Sample Consensus Initial Alignment (SAC-IA) is a commonly used point cloud-based method to achieve such a goal. However, its efficiency is low, and it cannot be applied in real-time applications. This paper analyzes the most time-consuming part of the SAC-IA algorithm: sample generation and evaluation. We propose two improvements to increase efficiency. In the initial aligning stage, instead of sampling the key points, the correspondence pairs between model and scene key points are generated in advance and chosen in each iteration, which reduces the redundant correspondence search operations; a geometric filter is proposed to prevent the invalid samples to the evaluation process, which is the most time-consuming operation because it requires transforming and calculating the distance between two point clouds. The introduction of the geometric filter can significantly increase the sample quality and reduce the required sample numbers. Experiments are performed on our own datasets captured by Kinect v2 Camera and on Bologna 1 dataset. The results show that the proposed method can significantly increase (10–30×) the efficiency of the original SAC-IA method without sacrificing accuracy.

Highlights

Object recognition and localization are essential functionalities for autonomous robot working in unstructured applications [1]; generally, fast and accurate 3D posture information retrieval is important to tasks, such as material handling, manipulation, assembly, welding, etc [2]
According to the used features, there are two kinds of pipelines for recognizing 3D objects from point clouds: the global pipeline and the local pipeline. Global feature descriptors, such as ensemble of shape functions (ESF) [4], global fast point feature histogram (GFPFH) [5], Viewport Feature Histogram (VFH) [6], and cluster view feature histogram (CVFH) [7], can be used to evaluate an object observed from different view angles
This paper studies the problem of recognizing 3D objects from cluttered scenes with point cloud information

Summary

Introduction

According to the used features, there are two kinds of pipelines for recognizing 3D objects from point clouds: the global pipeline and the local pipeline. Global feature descriptors, such as ensemble of shape functions (ESF) [4], global fast point feature histogram (GFPFH) [5], Viewport Feature Histogram (VFH) [6], and cluster view feature histogram (CVFH) [7], can be used to evaluate an object observed from different view angles. Global feature-based algorithms are efficient in computation time and memory consumption These methods require segmenting the object point cloud from the scene and are difficult to be applied in a cluttered environment with occlusion and complex foreground and background [9]

Objectives

Methods

Conclusion