63 citations · 151 across the 21 of their papers we have counts for
5 papers · 2 filters
"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences
Wout Boerdijk, Martin Sundermeyer, Maximilian Durner +1
We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed…
DOT: Dynamic Object Tracking for Visual SLAM
Irene Ballester, Alejandro Fontan, Javier Civera +2
In this paper we present DOT (Dynamic Object Tracking), a front-end that added to existing SLAM systems can significantly improve their robustness and accuracy in highly dynamic en…
Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry
Kashmira Shinde, Jongseok Lee, Matthias Humt +2
This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in th…
Segmentation of Surgical Instruments for Minimally-Invasive Robot-Assisted Procedures Using Generative Deep Neural Networks
Iñigo Azqueta-Gavaldon, Florian Fröhlich, Klaus Strobl +1
This work proves that semantic segmentation on minimally invasive surgical instruments can be improved by using training data that has been augmented through domain adaptation. The…
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Wout Boerdijk, Martin Sundermeyer, Maximilian Durner +1
Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time…