10 citations · 10 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments
Laura Alejandra Encinar Gonzalez, John Folkesson, Rudolph Triebel +1
Robust loop closure detection is a critical component of Simultaneous Localization and Mapping (SLAM) algorithms in GNSS-denied environments, such as in the context of planetary ex…
Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients
Simon Kristoffersson Lind, Rudolph Triebel, Volker Krüger
Modern robotic perception is highly dependent on neural networks. It is well known that neural network-based perception can be unreliable in real-world deployment, especially in di…
Single-Shot Metric Depth from Focused Plenoptic Cameras
Blanca Lasheras-Hernandez, Klaus H. Strobl, Sergio Izquierdo +3
Metric depth estimation from visual sensors is crucial for robots to perceive, navigate, and interact with their environment. Traditional range imaging setups, such as stereo or st…
Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments
Alberto García-Hernández, Riccardo Giubilato, Klaus H. Strobl +2
Perceptual aliasing and weak textures pose significant challenges to the task of place recognition, hindering the performance of Simultaneous Localization and Mapping (SLAM) system…
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
Maria Lyssenko, Christoph Gladisch, Christian Heinzemann +2
Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes ut…
A Safety-Adapted Loss for Pedestrian Detection in Automated Driving
Maria Lyssenko, Piyush Pimplikar, Maarten Bieshaar +2
In safety-critical domains like automated driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As common evaluation metrics a…