Abstract
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.
Abstract (translated)
具有高清晰度地图和准确的路级信息对于自动驾驶至关重要，但创建这些地图是一个资源密集的过程。为此，我们提出了一个成本有效的解决方案，使用仅是全球导航卫星系统（GNSS）和车辆上的摄像机来创建道路级地图。我们的解决方案利用了预定义的标准定义（SD）地图、GNSS测量、视觉观测里程和车道标记边缘检测点，同时估计车辆的6D姿态，其在SD地图上的位置以及交通线的3D几何形状。这是通过使用贝叶斯同时定位和多对象跟踪滤波器实现的，其中交通线的估计作为一个多扩展对象跟踪问题，通过轨迹的概率Multi-Bernoulli混合（TPMBM）滤波器求解。在TPMBM滤波器中，交通线通过B-spline轨迹建模，并且每个轨迹由一系列控制点参数化。所提出的解决方案已通过在高速公路上行驶的测试车辆的实验数据进行了评估。初步结果表明，交通线估计，叠加在卫星图像上，通常与车道标记在横向偏移量上相符。
URL
https://arxiv.org/abs/2405.04290
