71 citations · 228 across the 26 of their papers we have counts for
12 papers · 1 filter
Learning to Localize in New Environments from Synthetic Training Data
Dominik Winkelbauer, Maximilian Denninger, Rudolph Triebel
Most existing approaches for visual localization either need a detailed 3D model of the environment or, in the case of learning-based methods, must be retrained for each new scene.…
"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…
Bayesian Optimization Meets Laplace Approximation for Robotic Introspection
Matthias Humt, Jongseok Lee, Rudolph Triebel
In robotics, deep learning (DL) methods are used more and more widely, but their general inability to provide reliable confidence estimates will ultimately lead to fragile and unre…
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…
Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments
Cedric Le Gentil, Mallikarjuna Vayugundla, Riccardo Giubilato +3
The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these system…
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…
Effective Version Space Reduction for Convolutional Neural Networks
Jiayu Liu, Ioannis Chiotellis, Rudolph Triebel +1
In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis. However, many methods for neural networks…
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Jongseok Lee, Matthias Humt, Jianxiang Feng +1
We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariat…
Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper
Anko Börner, Heinz-Wilhelm Hübers, Odej Kao +15
Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and…
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…
Visual-Inertial Telepresence for Aerial Manipulation
Jongseok Lee, Ribin Balachandran, Yuri S. Sarkisov +6
This paper presents a novel telepresence system for enhancing aerial manipulation capabilities. It involves not only a haptic device, but also a virtual reality that provides a 3D…
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…