This paper presents EdgeLoc, an infrastructure-assisted, real-time localization system for autonomous driving that addresses the incompatibility between traditional localization methods and deep learning approaches. The system is built on top of the Robot Operating System (ROS) and combines the real-time performance of traditional methods with the high accuracy of deep learning approaches. The system leverages edge computing capabilities of roadside units (RSUs) for precise localization to enhance on-vehicle localization that is based on the real-time visual odometry. EdgeLoc is a parallel processing system, utilizing a proposed uncertainty-aware pose fusion solution. It achieves communication adaptivity through online learning and addresses fluctuations via window-based detection. Moreover, it achieves optimal latency and maximum improvement by utilizing auto-splitting vehicle-infrastructure collaborative inference, as well as online distribution learning for decision-making. Even with the most basic end-to-end deep neural network for localization estimation, EdgeLoc realizes a 67.75\% reduction in the localization error for real-time local visual odometry, a 29.95\% reduction for non-real-time collaborative inference, and a 30.26\% reduction compared to Kalman filtering. Finally, accuracy-to-latency conversion was experimentally validated, and an overall experiment was conducted on a practical cellular network. The system is open sourced at https://github.com/LoganCome/EdgeAssistedLocalization.

翻译：本文提出EdgeLoc，一种面向自动驾驶的、基础设施辅助的实时定位系统，旨在解决传统定位方法与深度学习途径之间的不兼容性。该系统基于机器人操作系统（ROS）构建，融合了传统方法的实时性能与深度学习途径的高精度特性。系统利用路侧单元（RSU）的边缘计算能力实现精确的定位，从而增强基于实时视觉里程计的车辆端定位。EdgeLoc是一个并行处理系统，采用所提出的不确定性感知位姿融合方案。它通过在线学习实现通信自适应性，并利用基于窗口的检测机制应对波动问题。此外，系统通过采用自动分割的车-基础设施协同推理以及面向决策的在线分布学习，实现了最优延迟和最大性能提升。即使使用最基本的端到端深度神经网络进行定位估计，EdgeLoc也能将实时局部视觉里程计的定位误差降低67.75%，非实时协同推理的定位误差降低29.95%，相较于卡尔曼滤波降低30.26%。最后，通过实验验证了精度-延迟转换的有效性，并在实际蜂窝网络上进行了整体实验。该系统已在https://github.com/LoganCome/EdgeAssistedLocalization开源。