In this paper, we present a global navigation satellite system (GNSS) aided LiDAR-visual-inertial scheme, RailLoMer-V, for accurate and robust rail vehicle localization and mapping. RailLoMer-V is formulated atop a factor graph and consists of two subsystems: an odometer assisted LiDAR-inertial system (OLIS) and an odometer integrated Visual-inertial system (OVIS). Both the subsystem exploits the typical geometry structure on the railroads. The plane constraints from extracted rail tracks are used to complement the rotation and vertical errors in OLIS. Besides, the line features and vanishing points are leveraged to constrain rotation drifts in OVIS. The proposed framework is extensively evaluated on datasets over 800 km, gathered for more than a year on both general-speed and high-speed railways, day and night. Taking advantage of the tightly-coupled integration of all measurements from individual sensors, our framework is accurate to long-during tasks and robust enough to grievously degenerated scenarios (railway tunnels). In addition, the real-time performance can be achieved with an onboard computer.

翻译：在本文中,我们提出了一个全球导航卫星系统(GNSS)辅助LiDAR视听内线系统(LailLoMer-V),用于准确和稳健的铁路车辆定位和绘图。铁路LailLoMer-V是在一个因子图上制作的,由两个子系统组成:高计辅助LiDAR内皮系统(OLIS)和高计综合视觉内皮系统(OVIS)。这两个子系统都利用了铁路上的典型几何结构。提取的铁路轨迹的飞机限制被用来补充OLIS的旋转和垂直错误。此外,线特征和消失点被用来限制OVIS的旋转漂移。对800公里以上的数据集进行了广泛的评价,这些数据集在全程和高速铁路上、日夜以夜为单位收集超过一年的时间。利用单个传感器所有测量结果的紧密结合,我们的框架准确到长期任务,并且坚固到足以严重损坏的机舱面情景(铁路隧道),此外,实时性表现可以通过计算机实现。