In this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.

中文翻译：

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多模式定位：LiDAR地图上的立体声

在本文中，我们介绍了一种用于自动驾驶汽车的实时高精度视觉定位系统，该系统仅使用低成本的立体摄像机通过使用较昂贵的3D LiDAR传感器构建的先验地图来定位汽车。为此，我们构造了两个不同的视觉图：用于基于视觉里程表（VO）的运动跟踪的稀疏特征视觉图，以及用于与先前的LiDAR图配准的半密集视觉图。为了注册来自不同模式（即摄像机和LiDAR）的两个点云，我们特别考虑了源点云的不确定性，利用概率加权正态分布变换（ProW-NDT）。然后通过姿态图优化融合配准结果，以校正VO漂移。此外，从先前的LiDAR映射中提取的surfel用于完善稀疏3D视觉特征，这将进一步改善基于VO的运动估计。所提出的系统已经在模拟和真实实验中进行了广泛的测试，表明可以实现鲁棒，高精度，实时的定位。