Robust Multi-Source Odometry Based on Cascaded Filtering and Hierarchical Optimization

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

Aiming at the trajectory drift and long-term computing power bottleneck of urban service robots in large-scale scenarios, this paper proposes a practical LIO-RTK-PGO multi-source fusion odometry. The front-end adopts a cascaded tightly coupled architecture, introducing RTK observation to correct the state in the filtering prediction stage to fundamentally suppress elevation and heading divergence; the back-end proposes hierarchical pose graph optimization (PGO), combining local high-frequency sliding window and global keyframe sparsification to control the computational complexity at O(1). Verified by real-vehicle tests and standard computing power platforms, the system eliminates long-range cumulative errors, providing a low-computing-power and highly reliable state estimation scheme for large-scale engineering implementation.

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