论文标题
自主驾驶的视觉定位：映射城市迷宫中的准确位置
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze
论文作者
论文摘要
准确的本地化是基础能力，是自动驾驶汽车完成其他任务（例如导航或路径规划）所必需的。 It is a common practice for vehicles to use GPS to acquire location information.但是，当车辆在内部城市内运行时，不同种类的结构可能会影响GPS信号并导致位置不准确的结果时，GP的应用可能会导致严重的挑战。 To address the localization challenges of urban settings, we propose a novel feature voting technique for visual localization.与传统的基于前景的方法不同，我们的方法采用了三个方向（前，左和右）的视图，因此可以显着提高位置预测的鲁棒性。在我们的工作中，我们将提出的功能投票方法制作到三个最先进的视觉本地化网络中，并正确修改其体系结构，以便将其应用于车辆操作。 Extensive field test results indicate that our approach can predict location robustly even in challenging inner-city settings.我们的研究阐明了使用视觉定位方法在理想的时间限制下帮助自动驾驶汽车在城市迷宫中找到准确的位置信息。
Accurate localization is a foundational capacity, required for autonomous vehicles to accomplish other tasks such as navigation or path planning. It is a common practice for vehicles to use GPS to acquire location information. However, the application of GPS can result in severe challenges when vehicles run within the inner city where different kinds of structures may shadow the GPS signal and lead to inaccurate location results. To address the localization challenges of urban settings, we propose a novel feature voting technique for visual localization. Different from the conventional front-view-based method, our approach employs views from three directions (front, left, and right) and thus significantly improves the robustness of location prediction. In our work, we craft the proposed feature voting method into three state-of-the-art visual localization networks and modify their architectures properly so that they can be applied for vehicular operation. Extensive field test results indicate that our approach can predict location robustly even in challenging inner-city settings. Our research sheds light on using the visual localization approach to help autonomous vehicles to find accurate location information in a city maze, within a desirable time constraint.
