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，一种面向基础设施辅助自动驾驶的实时定位系统，旨在解决传统定位方法与深度学习方法之间的不兼容性问题。该系统构建于机器人操作系统（ROS）之上，融合了传统方法的实时性能与深度学习的高精度特性。系统利用路侧单元（RSU）的边缘计算能力进行精确位置解算，以增强基于实时视觉里程计的车载定位性能。EdgeLoc采用并行处理架构，结合提出的不确定性感知位姿融合方案，通过在线学习实现通信自适应，并利用基于窗口的检测机制应对网络波动。此外，系统通过自动分割车-路协同推理及在线分布式学习决策机制，实现了最优延迟与最大性能提升。实验表明，即使采用最基础的端到端定位估计深度神经网络，EdgeLoc仍能实现实时局部视觉里程计定位误差降低67.75%，非实时协同推理误差降低29.95%，较卡尔曼滤波方法误差降低30.26%。研究最终通过实验验证了精度-延迟转换模型，并在实际蜂窝网络中完成了系统整体测试。系统代码已开源：https://github.com/LoganCome/EdgeAssistedLocalization。