Hierarchical semantic parsing for object pose estimation in densely cluttered scenes

Chi Li, Jonathan Bohren, Eric Carlson, Gregory D. Hager

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Densely cluttered scenes are composed of multiple objects which are in close contact and heavily occlude each other. Few existing 3D object recognition systems are capable of accurately predicting object poses in such scenarios. This is mainly due to the presence of objects with textureless surfaces, similar appearances and the difficulty of object instance segmentation. In this paper, we present a hierarchical semantic segmentation algorithm which partitions a densely cluttered scene into different object regions. A RANSAC-based registration method is subsequently applied to estimate 6-DoF object poses within each object class. Part of this algorithm includes a generalized pooling scheme used to construct robust and discriminative object representations from a convolutional architecture with multiple pooling domains. We also provide a new RGB-D dataset which serves as a benchmark for object pose estimation in densely cluttered scenes. This dataset contains five thousand scene frames and over twenty thousand labeled poses of ten common hand tools. We show that our method demonstrates improved performance of pose estimation on this new dataset compared with other state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5068-5075
Number of pages8
ISBN (Electronic)9781467380263
DOIs
StatePublished - Jun 8 2016
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: May 16 2016May 21 2016

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2016-June
ISSN (Print)1050-4729

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
CountrySweden
CityStockholm
Period5/16/165/21/16

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ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Li, C., Bohren, J., Carlson, E., & Hager, G. D. (2016). Hierarchical semantic parsing for object pose estimation in densely cluttered scenes. In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016 (pp. 5068-5075). [7487712] (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2016-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2016.7487712