信息与控制2025，Vol.54Issue(6)：917-928,12.DOI:10.13976/j.cnki.xk.2024.4722
基于边图重构权重模型的位姿图优化算法
Edge Graph Reconstructed Weight Model for Pose Graph Optimization Algorithm
摘要
Abstract
In simultaneous localization and mapping(SLAM)systems based on graph optimization,the presence of erroneous closed-loop edges can interfere with the convergence of the graph optimizer,leading to a decrease in optimization speed and thus reducing the accuracy and robustness of the SLAM system.Therefore,we propose a pose graph optimization algorithm based on edge graph reconstructed weight model for erroneous closed-loop edges(EGR-PGO),which effectively im-proves the robustness of PGO algorithm.The algorithm introduces an edge graph transformation model and uses PageRank algorithm to dynamically adjust the parameters of the weight function,thereby optimizing the weights of closed-loop edges.In each iteration process,the algorithm will remove the erroneous closed-loop edges again based on the change in residuals and the length of the closed-loop edges to reduce the interference of erroneous closed-loop edges on the optimization process.Finally,we conduct Monte Carlo experiments on the PGO dataset,and the experimental results verify the speed and robustness of the EGR-PGO algorithm,as well as its effectiveness in the presence of error-loop-closure edges.关键词
SLAM/位姿图优化/错误闭环边/边图/PageRank算法Key words
SLAM/pose graph optimization/error-loop-closure edge/edge graph/PageRank algorithm分类
信息技术与安全科学引用本文复制引用
郑劲康,魏国亮,蔡洁,冒凡,简单..基于边图重构权重模型的位姿图优化算法[J].信息与控制,2025,54(6):917-928,12.基金项目
国家自然科学基金项目(62273239) （62273239）
上海市"科技创新行动计划"国内科技合作项目(20015801100) （20015801100）
