Simultaneous Location of Rail Vehicles and Mapping of Environment with Multiple LiDARs

TL;DR

This paper presents a multi-LiDAR SLAM system for railway environments that enhances localization and mapping accuracy through preprocessing, plane constraints, and online extrinsic refinement, validated over 3000 km of data.

Contribution

It introduces a novel multi-LiDAR SLAM approach with rail track constraints and online extrinsic calibration for improved railway environment mapping.

Findings

Achieves accurate and robust localization in large-scale environments

Successfully applied to freight railway monitoring tasks

Validated over 3000 km of real-world railway data

Abstract

Precise and real-time rail vehicle localization as well as railway environment monitoring is crucial for railroad safety. In this letter, we propose a multi-LiDAR based simultaneous localization and mapping (SLAM) system for railway applications. Our approach starts with measurements preprocessing to denoise and synchronize multiple LiDAR inputs. Different frame-to-frame registration methods are used according to the LiDAR placement. In addition, we leverage the plane constraints from extracted rail tracks to improve the system accuracy. The local map is further aligned with global map utilizing absolute position measurements. Considering the unavoidable metal abrasion and screw loosening, online extrinsic refinement is awakened for long-during operation. The proposed method is extensively verified on datasets gathered over 3000 km. The results demonstrate that the proposed system achieves accurate and robust localization together with effective mapping for large-scale environments. Our system has already been applied to a freight traffic railroad for monitoring tasks.