Localization is a fundamental requirement for intelligent vehicles. Conventional localization methods usually suffer from various limitations, such as low accuracy and blocked areas for Global Positioning System, high cost for inertial navigation system or light detection and ranging, and low robustness for visual simultaneous localization and mapping or visual odometry. To overcome these problems, we propose a novel localization method integrated with a sparse visual map and a high-speed pavement visual odometry. We use a lateral-view camera to sense the sparse visual map node for accurate map-based localization. We use a down-view high-speed camera for odometry computation between two sparse visual map nodes. With a high-speed camera, it is possible to extract and track pavement features with stable resolution imaging even in high-speed movement. We also develop a data-driven motion model for the Kalman filter to fuse the localization results from the sparse map and the high-speed pavement visual odometry to enhance vehicle localization. The proposed method was tested in two different scenarios in different pavement conditions. The experimental results demonstrate that the proposed method can improve vehicle localization with low cost and high feasibility.

中文翻译：

---

通过融合稀疏视觉地图和高速路面视觉里程计改进智能车辆自定位

本地化是智能汽车的基本要求。传统的定位方法通常受到各种限制，例如全球定位系统的精度低和有遮挡的区域，惯性导航系统或光检测和测距的成本高，视觉同步定位和测绘或视觉测距的鲁棒性低。为了克服这些问题，我们提出了一种结合稀疏视觉地图和高速路面视觉里程计的新型定位方法。我们使用横向摄像头来感知稀疏的视觉地图节点，以实现基于地图的准确定位。我们使用下视图高速相机在两个稀疏视觉地图节点之间进行里程计计算。使用高速相机，即使在高速运动中，也可以以稳定的分辨率成像提取和跟踪路面特征。我们还为卡尔曼滤波器开发了一个数据驱动的运动模型，以融合稀疏地图和高速路面视觉里程计的定位结果，以增强车辆定位。所提出的方法在不同路面条件下的两种不同场景中进行了测试。实验结果表明，所提出的方法能够以低成本和高可行性提高车辆定位。