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Publication type: Article in Proceedings
Author: Jesse Richter-Klug, Udo Frese
Title: Towards Meaningful Uncertainty Information for CNN Based 6D Pose Estimates
Book / Collection title: International Conference on Computer Vision Systems
Page(s): 408 – 422
Year published: 2019
Publisher: Springer
Abstract: Image based object recognition and pose estimation is nowadays a heavily focused research field important for robotic object manipulation. Despite the impressive recent success of CNNs to our knowledge none includes a self-estimation of its predicted pose’s uncertainty.

In this paper we introduce a novel fusion-based CNN output architecture for 6d object pose estimation obtaining competitive performance on the YCB-Video dataset while also providing a meaningful uncertainty information per 6d pose estimate. It is motivated by the recent success in semantic segmentation, which means that CNNs can learn to know what they see in a pixel. Therefore our CNN produces a per-pixel output of a point in object coordinates with image space uncertainty, which is then fused by (generalized) PnP resulting in a 6d pose with 6×6 covariance matrix. We show that a CNN can compute image space uncertainty while the way from there to pose uncertainty is well solved analytically. In addition, the architecture allows to fuse additional sensor and context information (e.g. binocular or depth data) and makes the CNN independent of the camera parameters by which a training sample was taken.
Internet: https://link.springer.com/chapter/10.1007/978-3-030-34995-0_37
PDF Version: http://www.informatik.uni-bremen.de/agebv2/downloads/published/richterklugicvs19_final.pdf
Status: Reviewed
Last updated: 07. 02. 2022

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