High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles. Our proposed solution utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points, to simultaneously estimate the vehicle's 6D pose, its position within a SD map, and also the 3D geometry of traffic lines. This is achieved using a Bayesian simultaneous localization and multi-object tracking filter, where the estimation of traffic lines is formulated as a multiple extended object tracking problem, solved using a trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are modeled using B-spline trajectories, and each trajectory is parameterized by a sequence of control points. The proposed solution has been evaluated using experimental data collected by a test vehicle driving on highway. Preliminary results show that the traffic line estimates, overlaid on the satellite image, generally align with the lane markings up to some lateral offsets.

翻译：高精度车道级信息的高清地图对自动驾驶至关重要，但其创建过程是资源密集型的。为此，我们提出一种经济高效的解决方案，仅利用客户车辆上的全球导航卫星系统（GNSS）与摄像头即可构建车道级道路地图。本方案利用先验标准定义（SD）地图、GNSS测量值、视觉里程计以及车道标记边缘检测点，同步估计车辆的六自由度位姿、其在SD地图中的位置以及交通标线的三维几何形态。该过程通过贝叶斯同步定位与多目标跟踪滤波器实现，其中交通标线的估计被建模为多扩展目标跟踪问题，并采用轨迹泊松多伯努利混合（TPMBM）滤波器求解。在TPMBM滤波中，交通标线采用B样条轨迹建模，每条轨迹由一系列控制点参数化。本方案已在高速公路测试车辆采集的实验数据上进行验证。初步结果表明，覆盖在卫星图像上的交通标线估计结果与车道标记大致对齐，仅存在轻微的横向偏移。