arXiv subjects

Luis Diener

Publications and source records attributed to Luis Diener.

3 recordsLinked to original sources

arXiv2025

Integration of Visual SLAM into Consumer-Grade Automotive Localization

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.

cs.RO↗

arXiv2025

Lateral Velocity Model for Vehicle Parking Applications

Automated parking requires accurate localization for quick and precise maneuvering in tight spaces. While the longitudinal velocity can be measured using wheel encoders, the estimation of the lateral velocity remains a key challenge due to the absence of dedicated sensors in consumer-grade vehicles. Existing approaches often rely on simplified vehicle models, such as the zero-slip model, which assumes no lateral velocity at the rear axle. It is well established that this assumption does not hold during low-speed driving and researchers thus introduce additional heuristics to account for differences. In this work, we analyze real-world data from parking scenarios and identify a systematic deviation from the zero-slip assumption. We provide explanations for the observed effects and then propose a lateral velocity model that better captures the lateral dynamics of the vehicle during parking. The model improves estimation accuracy, while relying on only two parameters, making it well-suited for integration into consumer-grade applications.

cs.RO↗

arXiv2025

Radar-Based Odometry for Low-Speed Driving

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 inertial-measurement units and wheel encoders require vehicle-specific calibration, making them costly for consumer-grade vehicles. To overcome this, we propose a radar-based simultaneous localization and mapping (SLAM) approach that fuses inertial and 4D radar measurements. Our approach tightly couples feature positions and Doppler velocities for accurate localization and robust data association. Key contributions include a tightly coupled radar-Doppler extended Kalman filter, multi-radar support and an information-based feature-pruning strategy. Experiments using both proprietary and public datasets demonstrate high-accuracy localization during low-speed driving.

cs.RO↗