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PGO is a high-dimensional non-convex optimization algorithm commonly used in the back-end optimization of SLAM, which is usually modeled as maximum likelihood estimation. Since the current PGO algorithm faces difficulties in improving speed while ensuring accuracy when optimizing large-scale noise datasets, this paper proposed a hierarchical pose graph optimization algorithm based on nested dissection algorithm for large noise datasets. The algorithm firstly established a χ² test model with different distance measures, and then removed outlier points. Secondly, it used nested dissection method to split the original pose graph into a set of subgraphs and extracted a skeleton graph from these subgraphs. The skeleton represented the abstract topology of the original SLAM problem. Then, the algorithm optimized the skeleton graph and completed the initialization. Finally, experimental evaluation on simulated and real pose graph datasets show that the proposed algorithm can improve the calculation speed and scalability of the algorithm without affecting the accuracy. [ABSTRACT FROM AUTHOR]

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