activity
20242026
collaborators

9 papers

cs.CV2026

Visual Relocalization from Sparse Views in Aliased and Low-Texture Environments via Novel View Synthesis

Maria Peribañez, Javier Civera, Rudolph Triebel +1

Visual localization becomes extremely challenging in planetary-like terrains characterized by low texture, perceptual aliasing, harsh illumination, and sparse, weakly overlapping v…

cs.RO2026

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…

cs.CV2026

TAPNext++: What's Next for Tracking Any Point (TAP)?

Sebastian Jung, Artem Zholus, Martin Sundermeyer +6

Tracking-Any-Point (TAP) models aim to track any point through a video which is a crucial task in AR/XR and robotics applications. The recently introduced TAPNext approach proposes…

cs.RO2026

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…

cs.CV2026

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…

cs.CV2026

The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments

Riccardo Giubilato, Marcus Gerhard Müller, Marco Sewtz +3

We release the S3LI Vulcano dataset, a multi-modal dataset towards development and benchmarking of Simultaneous Localization and Mapping (SLAM) and place recognition algorithms tha…