4 citations · 6 across the 7 of their papers we have counts for
8 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…
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…
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…
A Multi-body Tracking Framework - From Rigid Objects to Kinematic Structures
Manuel Stoiber, Martin Sundermeyer, Wout Boerdijk +1
Kinematic structures are very common in the real world. They range from simple articulated objects to complex mechanical systems. However, despite their relevance, most model-based…
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…