用视觉定位提升汽车陀螺仪校准，改善低配车定位精度
Integration of Visual SLAM into Consumer-Grade Automotive Localization
- 融合视觉SLAM与车辆横向动力学模型实现在线陀螺仪校准
- 实验显示陀螺仪校准精度显著提升，定位性能优于现有方法
- 适合关注车载定位、视觉融合系统的工程师和研究者
当前消费级车辆的自运动估计主要依赖轮速计和惯性测量单元（IMU），但受限于系统误差和标定问题。尽管视觉-惯性SLAM在机器人领域已成为标准，但在汽车自运动估计中的应用仍鲜有探索。本文研究如何将视觉SLAM集成到消费级车辆定位系统中以提升性能。提出一种融合视觉SLAM与车辆横向动力学模型的框架，在真实驾驶条件下实现陀螺仪的在线校准。实验结果表明，基于视觉的集成显著提升了陀螺仪校准精度，从而改善整体定位表现。在自有数据集和公开数据集上均验证了该方法的有效性，在公开基准上优于当前最先进方法。
原文摘要 · Abstract (English)
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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