用雷达和多普勒信息实现厘米级低速定位，无需车辆校准。
Radar-Based Odometry for Low-Speed Driving
- 融合雷达与惯性数据，紧耦合特征位置与速度
- 在低速场景下定位误差小于10厘米，支持多雷达系统
- 适合自动驾驶泊车、城市低速驾驶等场景
针对低速行驶与泊车场景中的汽车里程计问题，本文提出一种基于雷达的同步定位与地图构建（SLAM）方法。由于狭小空间和近距障碍物要求厘米级精度，传统依赖惯性测量单元和轮速编码器的方法需车辆特异性校准，成本较高。为此，本文提出一种融合惯性与4D雷达测量的紧耦合方案，通过联合优化特征位置与多普勒速度提升定位精度与数据关联鲁棒性。关键贡献包括：紧耦合雷达-多普勒扩展卡尔曼滤波器、多雷达支持及基于信息量的特征剪枝策略。在自有与公开数据集上的实验表明，该方法在低速行驶中实现了高精度定位，定位误差低于10厘米。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。