论文标题
AVP-SLAM：停车场自动驾驶汽车的语义视觉映射和本地化
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot
论文作者
论文摘要
自主的代客停车是自动驾驶汽车的特定应用。在这项任务中，车辆需要在狭窄，拥挤和受GPS的停车场中航行。准确的本地化能力非常重要。传统的基于视觉的方法因跟踪因无纹理区域，重复的结构和外观变化而导致的跟踪损失。在本文中，我们利用强大的语义功能来构建地图并将车辆定位在停车场中。语义特征包含指南标志，停车线，速度凸起等，通常出现在停车场中。与传统功能相比，这些语义特征对于透视和照明的变化是长期稳定且健壮的。我们采用四个环绕视图摄像机来增加感知范围。在IMU（惯性测量单元）和车轮编码器的协助下，提出的系统生成了全局视觉语义图。该地图进一步用于将车辆定位在厘米水平上。我们分析了系统的准确性和回忆，并将其与实际实验中的其他方法进行比较。此外，我们通过自动停车申请证明了拟议系统的实用性。
Autonomous valet parking is a specific application for autonomous vehicles. In this task, vehicles need to navigate in narrow, crowded and GPS-denied parking lots. Accurate localization ability is of great importance. Traditional visual-based methods suffer from tracking lost due to texture-less regions, repeated structures, and appearance changes. In this paper, we exploit robust semantic features to build the map and localize vehicles in parking lots. Semantic features contain guide signs, parking lines, speed bumps, etc, which typically appear in parking lots. Compared with traditional features, these semantic features are long-term stable and robust to the perspective and illumination change. We adopt four surround-view cameras to increase the perception range. Assisting by an IMU (Inertial Measurement Unit) and wheel encoders, the proposed system generates a global visual semantic map. This map is further used to localize vehicles at the centimeter level. We analyze the accuracy and recall of our system and compare it against other methods in real experiments. Furthermore, we demonstrate the practicability of the proposed system by the autonomous parking application.
