使用Catmull-Rom样条的在线单目车道建图
Online Monocular Lane Mapping Using Catmull-Rom Spline
AI总结：
提出一种仅用单相机和里程计的在线单目车道建图方法，将车道关联建模为二分图分配问题，结合多因素设权并优化样条构建，在OpenLane数据集验证可提升建图与关联精度，且已开源代码。
摘要：
本研究提出了一种仅依赖单相机和里程计生成基于样条的地图的在线单目车道建图方法。该技术将车道关联过程建模为利用二分图的分配问题，通过结合Chamfer距离、位姿不确定性和横向序列一致性为边分配权重。此外，我们精心设计了控制点初始化、样条参数化和优化步骤，以逐步创建、扩展和细化样条。与以往使用自建数据集评估性能的研究不同，我们的实验在公开可用的OpenLane数据集上开展。实验结果表明，所提方法提升了车道关联和里程计精度，以及整体车道地图质量。我们已开源该项目的代码。
英文摘要：
In this study, we introduce an online monocular lane mapping approach that solely relies on a single camera and odometry for generating spline-based maps. Our proposed technique models the lane association process as an assignment issue utilizing a bipartite graph, and assigns weights to the edges by incorporating Chamfer distance, pose uncertainty, and lateral sequence consistency. Furthermore, we meticulously design control point initialization, spline parameterization, and optimization to progressively create, expand, and refine splines. In contrast to prior research that assessed performance using self-constructed datasets, our experiments are conducted on the openly accessible OpenLane dataset. The experimental outcomes reveal that our suggested approach enhances lane association and odometry precision, as well as overall lane map quality. We have open-sourced our code1 for this project.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
