Accurate, continuous, and reliable positioning is a critical component of achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of a stand-alone sensor and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view images segmentation algorithm based on Fully Convolutional Network (FCN) is proposed for GNSS NLOS detection. Building upon this, a novel NLOS detection and mitigation algorithm (named S-NDM) is extended to the tightly coupled Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU), and visual feature system which is called Sky-GVIO, with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system harmonizes Single Point Positioning (SPP) with Real-Time Kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of S-NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP-related and RTK-related models. The results exhibit that Sky-GVIO system achieves meter-level accuracy under SPP mode and sub-decimeter precision with RTK, surpassing the performance of GNSS/INS/Vision frameworks devoid of S-NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration at https://github.com/whuwangjr/sky-view-images .

翻译：精确、连续且可靠的定位是实现自动驾驶的关键组成部分。然而，在复杂的城市峡谷环境中，单一传感器的脆弱性以及高层建筑、树木和高架结构引起的非视距传播会严重影响定位结果。为应对这些挑战，本文提出了一种基于全卷积网络的天空视野图像分割算法，用于GNSS非视距信号检测。在此基础上，一种新颖的非视距检测与抑制算法（命名为S-NDM）被扩展应用于紧耦合的全球导航卫星系统、惯性测量单元与视觉特征系统（称为Sky-GVIO），旨在实现城市峡谷环境中的连续精确定位。此外，该系统融合了单点定位与实时动态测量方法，以增强其操作适应性与鲁棒性。在城市峡谷环境中，本文评估了所提S-NDM算法在不同紧耦合单点定位及实时动态测量模型下的定位性能。结果表明，Sky-GVIO系统在单点定位模式下达到米级精度，在实时动态测量模式下达到亚分米级精度，其性能优于未集成S-NDM的GNSS/INS/视觉组合框架。同时，包含训练集与评估集的天空视野图像数据集已公开发布，供学术研究使用，访问地址为 https://github.com/whuwangjr/sky-view-images。