14 citations · 16 across the 9 of their papers we have counts for
12 papers
Retrieve, Reproduce, Reveal: Dissecting Retrieval-Augmented Software Vulnerability Detection
Sabrina Kaniewski, Tim Krämer, Julius Bächle +3
Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding predictions in retrieved vulnera…
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…
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…
Integration of Visual SLAM into Consumer-Grade Automotive Localization
Luis Diener, Jens Kalkkuhl, Markus Enzweiler
Accurate ego-motion estimation in consumer-grade vehicles currently relies on proprioceptive sensors, i.e. wheel odometry and IMUs, whose performance is limited by systematic error…
Lateral Velocity Model for Vehicle Parking Applications
Luis Diener, Jens Kalkkuhl, Markus Enzweiler
Automated parking requires accurate localization for quick and precise maneuvering in tight spaces. While the longitudinal velocity can be measured using wheel encoders, the estima…
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…
Radar-Based Odometry for Low-Speed Driving
Luis Diener, Jens Kalkkuhl, Markus Enzweiler
We address automotive odometry for low-speed driving and parking, where centimeter-level accuracy is required due to tight spaces and nearby obstacles. Traditional methods using in…
A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models
Sabrina Kaniewski, Fabian Schmidt, Markus Enzweiler +2
The increasing adoption of Large Language Models (LLMs) in software engineering has sparked interest in their use for software vulnerability detection. However, the rapid developme…
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…