计算机应用研究2024，Vol.41Issue(6)：1916-1920,5.DOI:10.19734/j.issn.1001-3695.2023.08.0386
基于嵌套剖分的位姿图分层优化算法
Hierarchical pose graph optimization algorithm based on nested dissection
摘要
Abstract
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 im-proving 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 x2 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 repre-sented 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.关键词
位姿图优化/嵌套剖分/噪声/x2检验/最大似然估计Key words
pose graph optimization/nested dissection/noise/x2 test/maximum-likelihood estimation分类
信息技术与安全科学引用本文复制引用
简单,魏国亮,蔡洁,王耀磊..基于嵌套剖分的位姿图分层优化算法[J].计算机应用研究,2024,41(6):1916-1920,5.基金项目
国家自然科学基金资助项目(62273239) （62273239）
