A Map-Matching-Basedlocalization Method for Patrol Robot in Petrochemical Plant
Baoding Zhou, Hao Cai, Junfu Yang, Jiasong Zhu, Song Chun Zhu, Wenyu Jiang · 2025
Abstract The precise localization of patrol robots in petrochemical plants is essential for operational efficiency and safety. Traditional localization methods often struggle in these environments due to dynamic conditions and complex structures. This paper introduces a new localization method that combines map-matching with LiDAR-inertial fusion to tackle these challenges. By integrating high-order inertial odometry with nonlinear geometric observers, the method enhances pose estimation accuracy and real-time performance. Key contributions include the development of a lightweight framework for embedded systems and the integration of advanced algorithms to improve accuracy and efficiency. Experiments in a simulated petrochemical environment show the method’s effectiveness, achieving a positioning error of just 2.6 cm and a pose estimation frequency of 50 Hz. These results demonstrate the method’s potential for reliable navigation and monitoring in complex industrial settings, contributing to safer and more sustainable operations.