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Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation

Our research on keypoint-based object localization for assembly automation published in Sensors. 

Summary: Assembly processes are in dire need of innovative and simple techniques to streamline automation. In this paper we discuss our approach to data-efficient learning for assembly automation in which we rely on the availability of geometrical characteristics of mechanical components on the production floors. We formulate pose estimation in the context of Bayesian statistics and improve data-efficiency of a deep network trained on limited data.  

CVPR 2023

Our paper "Anomaly Detection with Domain Adaptation" is accepted to CVPR 2023. Congratulations to Ziyi Yang and Eric Darve, our Collaborators at Stanford.  Looking forward to discussing our work. 

NeurIPS 2022

We will discuss our paper "One shot learning of visual path navigation for autonomous vehicles" at NeurIPS 2022, Autonomous Driving workshop. This work was led by Amin Ghafourian and Debo Shi from LARA and our collaborators at Ford Motor Company.