In this study, we present a low-cost and unified framework for vectorized road mapping leveraging enhanced inverse perspective mapping (IPM). In this framework, Catmull-Rom splines are utilized to characterize lane lines, and all the other ground markings are depicted using polygons uniformly. The results from instance segmentation serve as references to refine the three-dimensional position of spline control points and polygon corner points. In conjunction with this process, the homography matrix of IPM and vehicle poses are optimized simultaneously. Our proposed framework significantly reduces the mapping errors associated with IPM. It also improves the accuracy of the initial IPM homography matrix and the predicted vehicle poses. Furthermore, it addresses the limitations imposed by the coplanarity assumption in IPM. These enhancements enable IPM to be effectively applied to vectorized road mapping, which serves a cost-effective solution with enhanced accuracy. In addition, our framework generalizes road map elements to include all common ground markings and lane lines. The proposed framework is evaluated in two different practical scenarios, and the test results show that our method can automatically generate high-precision maps with near-centimeter-level accuracy. Importantly, the optimized IPM matrix achieves an accuracy comparable to that of manual calibration, while the accuracy of vehicle poses is also significantly improved.

翻译：本研究提出了一种利用增强逆透视映射的低成本、统一矢量化道路地图生成框架。该框架采用Catmull-Rom样条表征车道线，并统一使用多边形描绘所有其他地面标线。实例分割的结果被用作优化样条控制点与多边形角点三维位置的参考依据。在此过程中，IPM的单应性矩阵与车辆位姿被同步优化。我们提出的框架显著降低了IPM相关的建图误差，同时提升了初始IPM单应性矩阵与预测车辆位姿的精度，并突破了IPM中平面假设的局限性。这些改进使得IPM能够有效应用于矢量化道路建图，成为一种兼具成本效益与高精度的解决方案。此外，本框架将地图要素泛化至包含所有常见地面标线与车道线。所提框架在两种不同实际场景中进行了评估，测试结果表明该方法能够自动生成接近厘米级精度的高精度地图。值得注意的是，优化后的IPM矩阵达到了与人工标定相当的精度，同时车辆位姿的精度也得到显著提升。