基于改进VINS-Mono算法的状态估计方法  

State Estimation Method Based on Improved VINS-Mono Algorithm

作　　者：王海芳 乔鼎杰 吴天浩 黄鹏 WANG Hai-fang;QIAO Ding-jie;WU Tian-hao;HUANG Peng(School of Control Engineering,Northeastern University at Qinhuangdao,Qinhuangdao 066004,China)

机构地区：[1]东北大学秦皇岛分校控制工程学院,河北秦皇岛066004

出　　处：《东北大学学报（自然科学版）》2024年第9期1287-1293,共7页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(51905080);河北省自然科学基金资助项目(F2024501008).

摘　　要：针对传统的即时定位并建图(simultaneous localization and mapping,SLAM)算法在位姿识别过程中对精度的要求,在VINS-Mono(visual inertial system-Mono)的前端部分,增加了逆向光流法;针对SLAM算法对实时性的要求,在VINS-Mono的后端滑窗优化方法中,融合了一种边缘化优化算法,再对除相机位姿的部分进行边缘化,然后边缘化相机的位姿部分,从而加速边缘化的过程.再使用EuRoc(European robotics challenge)数据集进行实验,结果发现针对前端的改进策略,精度提升不明显,并分析了原因;针对后端的改进策略,改进算法的边缘化时间平均减少了25.9%,又对比了改进算法与源码的轨迹精度,发现误差可控.最后验证了对VINS-Mono后端的改进策略在实时性上具有优越性.Aiming at the requirement of precision in pose recognition process of traditional SLAM(simultaneous localization and mapping),the reverse optical flow algorithm is added to the front‑end part of VINS-Mono.To meet the real‑time requirement of SLAM,a marginal optimization algorithm is integrated into the back‑end sliding window optimization method of VINS-Mono,the parts excluding camera pose are marginalized firstly,and then pose parts of cameras are marginalized,to accelerate marginalization process.And experiments are carried out using EuRoc(European robotics challenge)datasets.The results show that accuracy is improved slightly by improvement strategy of the front end,and the possible reasons are analyzed.For the improvement strategy of the back end,it is found that compared with the source code,the time used for the marginalization of the improved algorithm is reduced by 25.9%on average,and then compare their trajectory accuracy.It is found that the error is controllable.Finally,it is verified that the improved strategy for the back‑end of VINS-Mono is superior in real‑time performance.

关 键 词：状态估计方法 滑窗 边缘化 实时性 ROS(robot operating system)机器人仿真平台 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]