Work

High-precision urban rail map construction based on multi-sensor fusion

Year: 2024

Type: article

Abstract: The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems. However, the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision. Motivated by this, this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion. The algorithm integrates laser radar and Inertial Measurement Unit (IMU) data to construct the geometric structure map of the urban rail. It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information, thus generating high-precision maps. Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033 m for ground and tunnel scenes, respectively, representing a 19.31 % and 56.80 % improvement compared to state-of-the-art methods.

Source: High-speed Railway

DOI: 10.1016/j.hspr.2024.11.006

Authors: Zhihong Huang, Ruipeng Gao, Zejing Xu, Yiqing Liu, Zongru Ma, Dan Tao

Institution: Beijing Jiaotong University

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: diamond

Funder: Natural Science Foundation of Beijing Municipality