Rail Vehicle Localization and Mapping with LiDAR-Vision-Inertial-GNSS Fusion

TL;DR

This paper introduces RailLoMer-V, a GNSS-aided fusion scheme combining LiDAR, vision, inertial, and GNSS data for precise and robust rail vehicle localization and mapping, validated on extensive real-world datasets.

Contribution

The paper presents a novel factor graph-based framework integrating multiple sensor subsystems tailored for railway environments, enhancing accuracy and robustness over previous methods.

Findings

Achieves high accuracy over 800 km of diverse railway conditions.

Maintains robustness in challenging scenarios like tunnels.

Operates in real-time on onboard computing hardware.

Abstract

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.