In this paper, we present two distinct neural network-based pose estimation approaches for mobile manipulation in factory 
environments. Synthetic datasets, unique to the factory setting, are created for neural network training in each approach. Approach I uses a CNN in conjunction with RBG and depth images. Approach II uses the DOPE network along with RGB images, CAD dimensions of the objects of interest, and the PnP algorithm. Each approach is evaluated and compared across 
pipeline complexity, dataset preparation resources, robustness, platform and run-time resources, and post accuracy for manipulation planning. Finally, recommendations for when to use each method are provided.% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

Comparison of Neural Network-Based Pose Estimation Approaches for Mobile Manipulation

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

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