7 papers
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 topology-aware reasoning over spatial structure and dynamic inter…
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…
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…