Vehicle Localization in GPS-Denied Scenarios Using Arc-Length-Based Map Matching

TL;DR

This paper introduces an arc-length-based map matching method that leverages a digital map and vehicle kinematic data to correct drift in dead reckoning, enabling reliable vehicle localization in GPS-denied environments.

Contribution

The paper presents a novel arc-length-based map matching technique that integrates kinematic predictions with digital maps to improve localization accuracy without GPS.

Findings

Significant drift reduction in GPS-denied scenarios

Reliable vehicle localization demonstrated across tested environments

Enhanced safety and operational reliability for automated vehicles

Abstract

Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an arc-length-based map matching method that uses a digital 2D map of the scenario in order to correct drift in the dead reckoning estimate. The kinematic model's prediction is used to introduce a temporal notion to the spatial information available in the map data. Results show reliable improvement in drift for all GPS-denied scenarios tested in this study. This innovative approach ensures that automated vehicles can maintain continuous and reliable navigation, significantly enhancing their safety and operational reliability in environments where GPS signals are compromised or unavailable.