Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143044
Title: Efficient pose estimation from single RGB-D image via Hough forest with auto-context
Authors: Dong, Huixu
Prasad, Dilip Kumar
Yuan, Qilong
Zhou, Jiadong
Asadi, Ehsan
Chen, I-Ming
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Dong, H., Prasad, D. K., Yuan, Q., Zhou, J., Asadi, E., & Chen, I.-M. (2018). Efficient pose estimation from single RGB-D image via Hough forest with auto-context. Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7201-7206. doi:10.1109/iros.2018.8594064
Abstract: We propose a high efficient learning approach to estimating 6D (Degree of Freedom) pose of the textured or texture-less objects for grasping purposes in a cluttered environment where the objects might be partially occluded. The method comprises three main steps. Given a single RGB-D image, we first deploy appropriate features and the random forest to deduce the object class probability and cast votes for the 6D pose in Hough space by joint regression and classification framework, adopting reservoir sampling and summarizing the pose distribution by clustering. Next, we integrate the auto-context into cascaded Hough forests to improve the efficiency of learning. Extensive experiments on various public datasets and robotic grasps indicate that our method presents some improvements over the state-of-art and reveals the capability for estimating poses in practical applications efficiently.
URI: https://hdl.handle.net/10356/143044
ISBN: 978-1-5386-8095-7
DOI: 10.1109/iros.2018.8594064
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/iros.2018.8594064
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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