5 citations · 9 across the 14 of their papers we have counts for
16 papers
Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data
Marcus G Müller, Wout Boerdijk, Maximilian Durner +5
Terrain understanding is fundamental for mobile robots operating in unstructured outdoor environments. Existing vision-based traversability estimation methods rely on robot-specifi…
Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM
Markus Rueggeberg, Maximilian Ulmer, Maximilian Durner +4
The capability of multi-robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association…
Finding NeMO: A Geometry-Aware Representation of Template Views for Few-Shot Perception
Sebastian Jung, Leonard Klüpfel, Rudolph Triebel +1
We present Neural Memory Object (NeMO), a novel object-centric representation that can be used to detect, segment and estimate the 6DoF pose of objects unseen during training using…
Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation
Maximilian Ulmer, Wout Boerdijk, Rudolph Triebel +1
This paper presents OC-DiT, a novel class of diffusion models designed for object-centric prediction, and applies it to zero-shot instance segmentation. We propose a conditional la…
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
Maximilian Ulmer, Leonard Klüpfel, Maximilian Durner +1
We investigate the efficacy of data augmentations to close the domain gap in spaceborne computer vision, crucial for autonomous operations like on-orbit servicing. As the use of co…
Unknown Object Grasping for Assistive Robotics
Elle Miller, Maximilian Durner, Matthias Humt +5
We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based a…
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Jianxiang Feng, Matan Atad, Ismael Rodríguez +3
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i.e. whether they are feasible or not, to circumvent…
6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics
Maximilian Ulmer, Maximilian Durner, Martin Sundermeyer +2
We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model. To…
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
Matan Atad, Jianxiang Feng, Ismael Rodríguez +2
Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product cu…
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton +2
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a (…
Bayesian Active Learning for Sim-to-Real Robotic Perception
Jianxiang Feng, Jongseok Lee, Maximilian Durner +1
While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-c…
Unknown Object Segmentation from Stereo Images
Maximilian Durner, Wout Boerdijk, Martin Sundermeyer +3
Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known…
"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…
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…
Multi-path Learning for Object Pose Estimation Across Domains
Martin Sundermeyer, Maximilian Durner, En Yen Puang +4
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only d…
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner +2
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that i…