Autonomous vehicles rely on lane-level high-definition (HD) maps for self-localization and trajectory planning. Current mapping, however, relies on simple line models, while clothoid curves have unexplored potential. Clothoids, well known in road design, are often chosen to model the vehicle trajectory in planning and control systems as they describe the road with higher fidelity. For this reason, we propose two vision-based pipelines for generating lane-level HD maps using clothoid models. The first pipeline performs mapping with known poses, requiring precise real-time kinematics GPS (RTK GPS) measurements; the second copes with noisy localizations, solving the simultaneous localization and mapping (SLAM) problem. Both pipelines rely on a line detection algorithm to identify each line marking and perform a graph-based optimization to estimate the map.

中文翻译：

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基于回旋曲线的车道级高清地图：统一感知和控制模型

自动驾驶汽车依靠车道级高清 (HD) 地图进行自我定位和轨迹规划。然而，电流映射依赖于简单的线模型，而回旋曲线具有未开发的潜力。回旋曲线在道路设计中广为人知，在规划和控制系统中经常选择对车辆轨迹进行建模，因为它们以更高的保真度描述道路。出于这个原因，我们提出了两种基于视觉的管道，用于使用回旋模型生成车道级高清地图。第一条管道执行已知姿态的映射，需要精确的实时运动学 GPS (RTK GPS) 测量；第二个处理嘈杂的定位，解决同时定位和映射（SLAM）问题。