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Researcher profile

Timo Kehrer

Timo Kehrer contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceCryptography and SecurityHuman-Computer InteractionMachine LearningRoboticsSoftware Engineering

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Published work

2 published item(s)

preprint2026arXiv

MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality

Operating a multi-robot fleet for simultaneous localization and mapping (SLAM) in applications such as building inspection or warehouse-aisle monitoring requires the operator to maintain spatial awareness of each robot's position and mapping state, a task that scales poorly on conventional 2D interfaces. We present MR-SLAM, a mixed reality (MR) system in which an operator wearing a Meta Quest 3 headset teleoperates three simulated TurtleBot3 robots through a passthrough view with real-world occlusion, while spatially anchored dashboard panels report mapping progress in situ. Each robot runs an independent SLAM Toolbox instance whose occupancy grid is merged in real time on a Robot Operating System 2 (ROS 2) back end. Across five 9-minute evaluation sessions, the system delivered scans at 8.83 +/- 0.16 Hz, mapped 17.9 +/- 0.8 m^2 of merged occupancy, and reached 94.7 +/- 0.5% cross-instance occupancy consistency across robot pairs. An additional session recorded 6.3 ms median transform jitter and 26.7 m^2 coverage of a 41 m^2 grid. We position MR-SLAM as a reference implementation for combining passthrough mixed reality supervision with multi-robot SLAM on consumer hardware.

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preprint2022arXiv

VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python

Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated techniques. Objective: Such automated vulnerability detection techniques should achieve a high accuracy, point developers directly to the vulnerable code fragments, scale to real-world software, generalize across the boundaries of a specific software project, and require no or only moderate setup or configuration effort. Method: In this article, we present VUDENC (Vulnerability Detection with Deep Learning on a Natural Codebase), a deep learning-based vulnerability detection tool that automatically learns features of vulnerable code from a large and real-world Python codebase. VUDENC applies a word2vec model to identify semantically similar code tokens and to provide a vector representation. A network of long-short-term memory cells (LSTM) is then used to classify vulnerable code token sequences at a fine-grained level, highlight the specific areas in the source code that are likely to contain vulnerabilities, and provide confidence levels for its predictions. Results: To evaluate VUDENC, we used 1,009 vulnerability-fixing commits from different GitHub repositories that contain seven different types of vulnerabilities (SQL injection, XSS, Command injection, XSRF, Remote code execution, Path disclosure, Open redirect) for training. In the experimental evaluation, VUDENC achieves a recall of 78%-87%, a precision of 82%-96%, and an F1 score of 80%-90%. VUDENC's code, the datasets for the vulnerabilities, and the Python corpus for the word2vec model are available for reproduction. Conclusions: Our experimental results suggest...

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