用于低速行驶的基于雷达的里程计
Radar-Based Odometry for Low-Speed Driving
AI总结：
针对低速行驶和泊车场景对厘米级定位精度的需求，提出一种融合惯性测量与4D雷达测量的紧耦合SLAM方法，利用雷达-多普勒扩展卡尔曼滤波器及特征剪枝策略，实现了高精度定位。
摘要：
我们针对低速行驶和泊车中的汽车里程计问题进行研究，由于空间狭窄且附近有障碍物，这些场景需要厘米级精度。使用惯性测量单元和轮编码器的传统方法需要针对特定车辆进行校准，这使得它们对消费级车辆而言成本高昂。为克服此问题，我们提出了一种基于雷达的同步定位与建图（SLAM）方法，该方法融合了惯性测量与4D雷达测量。我们的方法将特征位置与多普勒速度紧密耦合，以实现精准定位和稳健的数据关联。主要贡献包括紧耦合的雷达-多普勒扩展卡尔曼滤波器、多雷达支持以及基于信息的特征剪枝策略。使用专有和公开数据集的实验表明，在低速行驶期间实现了高精度定位。
英文摘要：
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.
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