用多车次雷达点云优化定位，提升高精地图生成精度
Radar-based Pose Optimization for HD Map Generation from Noisy Multi-Drive Vehicle Fleet Data
- 通过雷达点云对齐与位姿图优化，降低车辆定位噪声
- 生成的全局雷达占位图特征清晰，尤其突出护栏柱等关键结构
- 可直接用于现有车道线地图生成，显著提升地图质量
高精地图对自动驾驶至关重要，但人工生成和维护成本高昂。为此，本文提出自动化地图生成方案，利用车队车辆提供的传感器数据，尽管其测量存在噪声。核心方法是基于不同行驶记录的原始雷达点云，对齐车辆姿态并进行位姿图优化，获得全局最优解。优化后的位姿首先用于构建全局雷达占位图，有助于车辆精准定位；定性分析显示，地图中护栏柱等特征对比度高、清晰可见。其次，这些改进的位姿可作为已有车道边界地图生成流程的基础，相较于原始纯线检测优化方法，大幅提升了地图输出质量。
原文摘要 · Abstract (English)
High-definition (HD) maps are important for autonomous driving, but their manual generation and maintenance is very expensive. This motivates the usage of an automated map generation pipeline. Fleet vehicles provide sufficient sensors for map generation, but their measurements are less precise, introducing noise into the mapping pipeline. This work focuses on mitigating the localization noise component through aligning radar measurements in terms of raw radar point clouds of vehicle poses of different drives and performing pose graph optimization to produce a globally optimized solution between all drives present in the dataset. Improved poses are first used to generate a global radar occupancy map, aimed to facilitate precise on-vehicle localization. Through qualitative analysis we show contrast-rich feature clarity, focusing on omnipresent guardrail posts as the main feature type observable in the map. Second, the improved poses can be used as a basis for an existing lane boundary map generation pipeline, majorly improving map output compared to its original pure line detection based optimization approach.
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