5 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…