实时融合多车观测，动态更新高精地图并提升定位精度
RTMap: Real-Time Recursive Mapping with Change Detection and Localization
- 通过自进化记忆机制，持续聚合多车行驶数据构建地图
- 实现高精地图元素的位置不确定性建模与道路结构变更实时检测
- 适合需要高可靠定位与地图更新的自动驾驶系统
现有在线高精地图方法虽缓解了离线流程负担并提升地图新鲜度，但仍受限于感知误差、密集交通下的遮挡以及多智能体观测融合能力不足。本文提出RTMap，通过持续众包多轮次行驶数据，构建可自我演化的高精地图作为先验记忆。在车载端，RTMap以端到端方式同时解决三个核心挑战：（1）高精地图元素的不确定性感知位置建模；（2）基于众包先验地图的概率化定位；（3）道路结构变化的实时检测。在多个公开自动驾驶数据集上的实验表明，该方法显著提升了先验地图质量与定位精度，在保障下游预测与规划模块鲁棒性的同时，异步渐进优化了众包先验地图的准确性和新鲜度。代码将开源于https://github.com/CN-ADLab/RTMap。
原文摘要 · Abstract (English)
While recent online HD mapping methods relieve burdened offline pipelines and solve map freshness, they remain limited by perceptual inaccuracies, occlusion in dense traffic, and an inability to fuse multi-agent observations. We propose RTMap to enhance these single-traversal methods by persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory. On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion: (1) Uncertainty-aware positional modeling for HD map elements, (2) probabilistic-aware localization w.r.t. the crowdsourced prior-map, and (3) real-time detection for possible road structural changes. Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy, demonstrating our effectiveness of robustly serving downstream prediction and planning modules while gradually improving the accuracy and freshness of the crowdsourced prior-map asynchronously. Our source-code will be made publicly available at https://github.com/CN-ADLab/RTMap.
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