A Triangulation-Based Visual Localization for Field Robots

Abstract

Localization under GPS shadowed areas is an important yet challenging task for field robot operation. In this study, we propose a novel visual localization method for field robots. Our method leverages triangulation views to accurately locate the robot in motion. We use one-stage feature extraction to effectively preserve local features for image representation and use a GMCP with flexible adaptive weights to manage features to triangulate the location prediction. The extensive experimental results indicate that our method is competitive with the existing state-of-the-art approaches and GPS.

Baijian Yang

Associate Dean for Research and Professor of Computer and Information Technology

My research interests include applied machine learning, big data and cybersecurity.