用单目相机和普通定位设备，实现高精度地图的众包生成。
B$^2$F-Map: Crowd-sourced Mapping with Bayesian B-spline Fusion
- 基于贝叶斯B样条融合，统一处理多车采集数据的不确定性。
- 在真实复杂路况下生成几何一致的车道级地图，无需先验高清地图。
- 适合自动驾驶地图众包更新，尤其适用于低成本车辆部署。
众包地图为传统测绘车辆提供了可扩展的替代方案。然而，现有方法或依赖先验高清地图，或忽略地图融合中的不确定性。本文提出完整的高清地图生成流程，仅需配备单目相机、消费级GNSS和惯性测量单元的量产车辆。方法包括云端轻量标准地图下的车辆定位、车载通过扩展目标轨迹（EOT）泊松-伯努利滤波与吉布斯采样进行地图构建，以及云端多车数据优化与贝叶斯地图融合。车道线采用B样条表示，每个B样条由高斯分布控制点参数化，并提出一种新型贝叶斯融合框架，能有效处理不同密度表示的样条轨迹，实现不确定性的合理建模。我们在多样驾驶条件下收集的大规模真实数据集上评估了B²F-Map，结果表明该方法可生成几何一致的车道级地图。
原文摘要 · Abstract (English)
Crowd-sourced mapping offers a scalable alternative to creating maps using traditional survey vehicles. Yet, existing methods either rely on prior high-definition (HD) maps or neglect uncertainties in the map fusion. In this work, we present a complete pipeline for HD map generation using production vehicles equipped only with a monocular camera, consumer-grade GNSS, and IMU. Our approach includes on-cloud localization using lightweight standard-definition maps, on-vehicle mapping via an extended object trajectory (EOT) Poisson multi-Bernoulli (PMB) filter with Gibbs sampling, and on-cloud multi-drive optimization and Bayesian map fusion. We represent the lane lines using B-splines, where each B-spline is parameterized by a sequence of Gaussian distributed control points, and propose a novel Bayesian fusion framework for B-spline trajectories with differing density representation, enabling principled handling of uncertainties. We evaluate our proposed approach, B$^2$F-Map, on large-scale real-world datasets collected across diverse driving conditions and demonstrate that our method is able to produce geometrically consistent lane-level maps.
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