2 citations · 4 across the 10 of their papers we have counts for
11 papers
Bootstrapping Self-Supervised Learning of Binary Classification Using Error Bounds: A Case Study on a Robotic Insertion Task
Zebin Duan, Norbert Krüger, Juan Heredia +2
Flexible manufacturing requires rapid deployment of solutions and minimal setup time to remain competitive. An essential attribute is the ability to control error levels, as failur…
A novel network for classification of cuneiform tablet metadata
Frederik Hagelskjær
In this paper, we present a network structure for classifying metadata of cuneiform tablets. The problem is of practical importance, as the size of the existing corpus far exceeds…
Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification
Zebin Duan, Frederik Hagelskjær, Aljaz Kramberger +2
The automation of robotic tasks requires high precision and adaptability, particularly in force-based operations such as insertions. Traditional learning-based approaches either re…
Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task
Frederik Hagelskjær, Kasper Høj Lorenzen, Dirk Kraft
The World Robot Summit 2018 Assembly Challenge included four different tasks. The kitting task, which required bin-picking, was the task in which the fewest points were obtained. H…
KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by Generalized Keypoint Matching
Frederik Hagelskjær, Rasmus Laurvig Haugaard
In this paper, we present KeyMatchNet, a novel network for zero-shot pose estimation in 3D point clouds. Our method uses only depth information, making it more applicable for many…
SpyroPose: SE(3) Pyramids for Object Pose Distribution Estimation
Rasmus Laurvig Haugaard, Frederik Hagelskjær, Thorbjørn Mosekjær Iversen
Object pose estimation is a core computer vision problem and often an essential component in robotics. Pose estimation is usually approached by seeking the single best estimate of…
In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components
Frederik Hagelskjaer, Dirk Kraft
The insertion of through-hole components is a difficult task. As the tolerances of the holes are very small, minor errors in the insertion will result in failures. These failures c…
ParaPose: Parameter and Domain Randomization Optimization for Pose Estimation using Synthetic Data
Frederik Hagelskjaer, Anders Glent Buch
Pose estimation is the task of determining the 6D position of an object in a scene. Pose estimation aid the abilities and flexibility of robotic set-ups. However, the system must b…
Deep learning classification of large-scale point clouds: A case study on cuneiform tablets
Frederik Hagelskjaer
This paper introduces a novel network architecture for the classification of large-scale point clouds. The network is used to classify metadata from cuneiform tablets. As more than…
Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization
Frederik Hagelskjaer, Anders Glent Buch
Since the introduction of modern deep learning methods for object pose estimation, test accuracy and efficiency has increased significantly. For training, however, large amounts of…
PointVoteNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds
Frederik Hagelskjær, Anders Glent Buch
We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data. Many recent learning-based approaches use primarily RGB information for detecting…