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.

翻译：众包地图构建为使用传统测绘车辆创建地图提供了一种可扩展的替代方案。然而，现有方法要么依赖于先验的高清地图，要么忽略了地图融合中的不确定性。在本工作中，我们提出了一套完整的流程，仅使用配备单目相机、消费级GNSS和IMU的量产车辆来生成高清地图。我们的方法包括：基于轻量级标准地图的云端定位、通过带有吉布斯采样的扩展目标轨迹泊松多伯努利滤波器的车载建图，以及云端多行程优化与贝叶斯地图融合。我们使用B样条表示车道线，其中每条B样条由一系列高斯分布的控制点参数化，并提出了一种新颖的贝叶斯融合框架，用于处理具有不同密度表示的B样条轨迹，从而实现对不确定性的原则性处理。我们在涵盖多种驾驶条件的大规模真实世界数据集上评估了所提出的方法B$^2$F-Map，并证明我们的方法能够生成几何一致的车道级地图。