50 citations · 55 across the 7 of their papers we have counts for
7 papers
FM-Loc: Using Foundation Models for Improved Vision-based Localization
Reihaneh Mirjalili, Michael Krawez, Wolfram Burgard
Visual place recognition is essential for vision-based robot localization and SLAM. Despite the tremendous progress made in recent years, place recognition in changing environments…
Audio Visual Language Maps for Robot Navigation
Chenguang Huang, Oier Mees, Andy Zeng +1
While interacting in the world is a multi-sensory experience, many robots continue to predominantly rely on visual perception to map and navigate in their environments. In this wor…
Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces
Johan Vertens, Nicolai Dorka, Tim Welschehold +2
The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a…
Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
Nicolai Dorka, Tim Welschehold, Wolfram Burgard
Early stopping based on the validation set performance is a popular approach to find the right balance between under- and overfitting in the context of supervised learning. However…
Learning and Aggregating Lane Graphs for Urban Automated Driving
Martin Büchner, Jannik Zürn, Ion-George Todoran +2
Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with co…
USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks
Johan Vertens, Wolfram Burgard
In this paper we propose USegScene, a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images using convol…