14 citations · 16 across the 7 of their papers we have counts for
8 papers
Relationally Grounded Latent World Models for Autonomous Driving
Fabian Schmidt, Markus Enzweiler, Abhinav Valada
Latent world models learn predictive representations for autonomous driving, but the relational semantics these states preserve often remain implicit. We investigate whether traffi…
Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots
Roberto Corlito, Fabian Schmidt, Nils Seibert +3
Autonomous navigation of quadrupedal robots in diverse environments fundamentally relies on resilient Simultaneous Localization and Mapping (SLAM). While visual-inertial SLAM has m…
LAD-Drive: Bridging Language and Trajectory with Action-Aware Diffusion Transformers
Fabian Schmidt, Karol Fedurko, Markus Enzweiler +1
While multimodal large language models (MLLMs) provide advanced reasoning for autonomous driving, translating their discrete semantic knowledge into continuous trajectories remains…
Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition
Fabian Schmidt, Noushiq Mohammed Kayilan Abdul Nazar, Markus Enzweiler +1
Large Language Models (LLMs) are increasingly used for decision-making and planning in autonomous driving, showing promising reasoning capabilities and potential to generalize acro…
GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving
Fabian Schmidt, Markus Enzweiler, Abhinav Valada
Vision-language models have recently emerged as promising planners for autonomous driving, where success hinges on reasoning over spatial structure and dynamic interactions from mu…
NeRF and Gaussian Splatting SLAM in the Wild
Fabian Schmidt, Markus Enzweiler, Abhinav Valada
Navigating outdoor environments with visual Simultaneous Localization and Mapping (SLAM) systems poses significant challenges due to dynamic scenes, lighting variations, and season…
ROVER: A Multi-Season Dataset for Visual SLAM
Fabian Schmidt, Julian Daubermann, Marcel Mitschke +4
Robust SLAM is a crucial enabler for autonomous navigation in natural, semi-structured environments such as parks and gardens. However, these environments present unique challenges…
Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing
Fabian Schmidt, Constantin Blessing, Markus Enzweiler +1
Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on exter…