A Simple yet Effective Subway Self-positioning Method based on Aerial-view Sleeper Detection

TL;DR

This paper introduces a low-cost, real-time subway self-positioning method using aerial-view sleeper detection with YOLOv8n, achieving high accuracy without extensive infrastructure.

Contribution

The paper presents a novel visual-assisted self-localization framework for subways that relies on aerial-view sleeper detection, reducing infrastructure costs and improving robustness.

Findings

Sleeper detection with F1-score of 0.929 at 1111 fps.

Positioning error of only 0.1%.

Effective for 6.9 km subway route.

Abstract

With the rapid development of urban underground rail vehicles,subway positioning, which plays a fundamental role in the traffic navigation and collision avoidance systems, has become a research hot-spot these years. Most current subway positioning methods rely on localization beacons densely pre-installed alongside the railway tracks, requiring massive costs for infrastructure and maintenance, while commonly lacking flexibility and anti-interference ability. In this paper, we propose a low-cost and real-time visual-assisted self-localization framework to address the robust and convenient positioning problem for subways. Firstly, we perform aerial view rail sleeper detection based on the fast and efficient YOLOv8n network. The detection results are then used to achieve real-time correction of mileage values combined with geometric positioning information, obtaining precise subway locations. Front camera Videos for subway driving scenes along a 6.9 km route are collected and annotated from the simulator for validation of the proposed method. Experimental results show that our aerial view sleeper detection algorithm can efficiently detect sleeper positions with F1-score of 0.929 at 1111 fps, and that the proposed positioning framework achieves a mean percentage error of 0.1\%, demonstrating its continuous and high-precision self-localization capability.