计算机应用研究2025，Vol.42Issue(1)：149-155,7.DOI:10.19734/j.issn.1001-3695.2024.04.0170
基于回环边残差聚焦权重模型的位姿图优化算法
Pose graph optimization algorithm based on loop-closure edges residual focusing weight model
摘要
Abstract
In graph-based SLAM systems,loop-closure edges with large noise may severely impede the optimizer from rapidly converging to the optimal solution,leading to a noticeable decrease in localization accuracy and map consistency.Therefore,the objective of this paper was to investigate robust methods for handling loop-closure edges,which was crucial for optimization algorithms in the presence of large noise.Toward this aim,this paper introduced a new concept of K-means clustering to clas-sify the residual values of loop-closure edges,thereby established a new residual threshold model.This model adaptively adjus-ted the weights of loop-closure edges during optimization to reduce their impact on the optimization process.Subsequently,the formulation of the residual weighted enhancement for recursive least squares pose graph optimization algorithm(RW-RLSPGO)was based on the iterative reweighted least squares principle.Finally,it conducted Monte Carlo experiments on both simulated and real pose graph optimization(PGO)datasets.The experimental results demonstrate a significant improvement in both accuracy and robustness with the RW-RLSPGO algorithm,validating its effectiveness in high-noise environments.关键词
同时定位与建图/位姿图优化/回环边/大噪声/聚类Key words
simultaneous localization and mapping(SLAM)/pose graph optimization/loop-closure edge/large noise/clus-tering分类
信息技术与安全科学引用本文复制引用
冒凡,魏国亮,蔡洁,郑劲康,简单..基于回环边残差聚焦权重模型的位姿图优化算法[J].计算机应用研究,2025,42(1):149-155,7.基金项目
国家自然科学基金资助项目(62273239) （62273239）
上海市"科技创新行动计划"国内 科技合作项目(20015801100) （20015801100）
