LIOP: Tightly Coupled LiDAR-Inertial Odometry and Prior Information System for Long-term Localization*

Localization is a crucial component of robotic systems. LiDAR odometry provides robust and real-time localization but suffers from significant cumulative errors in the absence of loop closures. On the other hand, local localization based on prior maps is not affected by cumulative errors but can fail when faced with dynamic changes in the map during long-term localization scenarios. To address these issues, we propose a tightly coupled LiDAR-inertial odometry(LIO) and prior information localization system that simultaneously solves the problems of cumulative error and localization failure in dynamic scenes. Our system employs local localization to quickly track the prior map, thereby reducing odometry’s cumulative error and ensuring robust performance even in areas with significant scene changes. Additionally, we introduce a global localization strategy to rapidly correct cumulative errors when odometry tracking of the prior map fails. We evaluated our approach on the MulRan dataset, and the results demonstrate that our method effectively resolves localization failures and cumulative error issues during long-term localization. Furthermore, we validated the feasibility of our method in a real-world logistics factory environment. The results indicate that our method performs well in complex and dynamic logistics factory scenarios.