Efficient pose estimation from single RGB-D image via Hough forest with auto-context
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...
Saved in:
Main Authors: | , , , , , |
---|---|
Other Authors: | |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/143044 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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. |
---|