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An NC Code Based Machining Movement Simulation Method for a Parallel Robotic Machine Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Fast 3D Object Recognition Pipeline in Cluttered and Occluded Scenes

Authors : Liupo Zheng, Hesheng Wang, Weidong Chen

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

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Abstract

In this paper we propose a framework for instance recognition and object localization in cluttered and occluded household environment for robot grasping task. The whole system bases on a coarse to fine pipeline in combination with the state-of-the-art methods of RGBD-based object detection. We build a sparse feature model by extracting structure key points incorporating texture cues in the train procedure. After that, the paper demonstrates how the algorithm decreases the time complexity and simultaneously guarantees the accuracy of the recognition and pose estimation. Quantitative experimental evaluations are presented using both acknowledged ground truth dataset and real-world robot perception system.

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previous chapter A Dual-Camera Assisted Method of the SCARA Robot for Online Assembly of Cellphone Batteries
next chapter Implementation of Multiple View Approach for Pose Estimation with an Eye-In-Hand Robotic System
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Metadata
Title
A Fast 3D Object Recognition Pipeline in Cluttered and Occluded Scenes
Authors
Liupo Zheng
Hesheng Wang
Weidong Chen
Copyright Year
2017
Book
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65291-7

Electronic ISBN: 978-3-319-65292-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65292-4_51

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