Autonomous Vehicle Pose Estimation in GNSS-Denied Areas Using Cross-Track Error Measurements

Accurate pose estimation is critical for the safe and efficient operation of autonomous vehicles, enabling precise navigation and control in highly dynamic environments. This study presents a novel approach to vehicle pose estimation using cross-track error (CTE) measurements as supplemental inputs for GNSS denied environments. The proposed method leverages CTE, a measure of lateral deviation from a desired trajectory, to estimate the vehicle’s position and orientation in real-time using an extended Kalman filter (EKF) framework. By integrating CTE into an existing pose estimation architecture, the new system is able to provide vehicle state estimates with a greater accuracy than conventional inertial navigation systems (INS). Simulation studies were conducted on benchmark test sets, demonstrating the method’s effectiveness at reducing pose estimation errors by up to 98% in various GNSS denied situations. This research contributes to the advancement of robust and cost-efficient localization strategies, paving the way for safer autonomous vehicle navigation.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Moomaw, Andrew Jacob

Chair dc:contributor.committeechair

• Southward, Steve C.

Committee members dc:contributor.committeemember

• Wicks, Alfred L.
• Abbott, A. Lynn

• Pose Estimation
• Cross-Track Error
• Sensor Fusion
• Extended Kalman Filtering

Statement dc:rights

• In Copyright (InC)

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10919/136908

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/136908