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arXiv · 2511.06919

Integration of Visual SLAM into Consumer-Grade Automotive Localization

Luis Diener·Jens Kalkkuhl·Markus Enzweiler
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Abstract

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 errors and calibration. While visual-inertial SLAM has become a standard in robotics, its integration into automotive ego-motion estimation remains largely unexplored. This paper investigates how visual SLAM can be integrated into consumer-grade vehicle localization systems to improve performance. We propose a framework that fuses visual SLAM with a lateral vehicle dynamics model to achieve online gyroscope calibration under realistic driving conditions. Experimental results demonstrate that vision-based integration significantly improves gyroscope calibration accuracy and thus enhances overall localization performance, highlighting a promising path toward higher automotive localization accuracy. We provide results on both proprietary and public datasets, showing improved performance and superior localization accuracy on a public benchmark compared to state-of-the-art methods.

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Luis Diener, Jens Kalkkuhl, Markus Enzweiler. 2025-11-10. Integration of Visual SLAM into Consumer-Grade Automotive Localization. https://arxiv.org/abs/2511.06919

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