多视角视觉-惯性融合的车间AGV精确导航方法  

Multi-view Visual-inertial Fusion for Precise AGV Navigation in Workshops

作　　者：王鑫[1] 李耕宇 曾子铭 高焕兵[3,4] 张吟龙 WANG Xin;LI Gengyu;ZENG Ziming;GAO Huanbing;ZHANG Yinong(School of Electrical and Control Engineering,Shenyang Jianzhu University,Shenyang 110168,China;School of Automotive and Transportation Engineering,Shenzhen Polytechnic,Shenzhen 518055,China;School of Information and Electrical Engineering,Shandong Jianzhu University,Ji’nan 250101,China;Key Laboratory of Intelligent Buildings Technology,Jinan 250101,China;Key Laboratory of Networked Control System,Chinese Academy of Sciences,Shenyang 110016,China;State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China)

机构地区：[1]沈阳建筑大学电气与控制工程学院,辽宁沈阳110168 [2]深圳职业技术学院汽车与交通学院,广东深圳518055 [3]山东建筑大学信息与电气工程学院,山东济南250101 [4]山东省智能建筑技术重点实验室,山东济南250101 [5]中国科学院网络化控制系统重点实验室,辽宁沈阳110016 [6]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016

出　　处：《机器人》2024年第4期476-487,共12页Robot

基　　金：国家自然科学基金(62273332);中国科学院青年创新促进会会员项目(2022201);广东省基础与应用基础研究基金(2023A1515011363)。

摘　　要：以实现车间AGV精确导航为目标,针对视觉-惯性融合定位方法存在的缺少绝对位姿、绝对尺度估计不准确、累积误差大等问题,提出一种多视角视觉-惯性紧耦合的实时定位方法。首先为了建立全局参考坐标系,实现长期漂移校正,设计了一种具有全局一致性的视觉-惯性融合AGV导航框架。然后针对视觉-惯性融合初始化过程中的尺度估计不准问题,提出多视角相机与IMU(惯性测量单元)联合初始化方法,通过最大后验概率模型得到更精准的初始化参数。针对跟踪估计部分存在的误差累积和惯性偏差漂移问题,提出二维码位姿修正模型,对部分关键帧进行周期性补偿。针对优化建图部分存在的位姿优化易陷入局部极值问题,提出位姿约束优化模型,提高AGV定位精度。搭建了车间AGV导航平台上对本文方法进行了验证,并与当前最先进的视觉-惯性导航方法进行比对,结果表明本文方法的时间效率与定位精度均明显优于对比方法;平移的均方根误差小于50 mm,旋转的均方根误差小于2?。Aiming to achieve precise navigation of AGVs(automated guided vehicles)in workshops,a real-time multi-view localization method with tightly-coupled visual-inertial fusion is proposed.This method address the challenges faced by existing visual-inertial fusion based localization methods,such as lack of absolute pose,inaccurate estimation of absolute scale,and significant cumulative errors.Firstly,a visual-inertial fusion based AGV navigation framework with global consis-tency is designed to establish a global reference coordinate system and achieve long-term drift correction.Next,a multi-view camera and IMU(inertial measurement unit)joint initialization method is proposed to tackle the issue of inaccurate scale estimation in the initialization phase of visual-inertial fusion.This method utilizes a maximum posteriori probability model to obtain more accurate initialization parameters.Furthermore,a QR(quick response)code based pose correction model is proposed to compensate some keyframes periodically,and thus mitigating the effects of error accumulation and inertial deviation drift in the tracking estimation part.In addition,a pose constraint optimization model is proposed to address the issue of local extremum in the optimization mapping part and improve AGV localization accuracy.Finally,the proposed method is validated on the constructed AGV navigation platform in a workshop and compared against state-of-the-art visual-inertial navigation methods.The results demonstrate the superiority of the proposed method in terms of time efficiency and positioning accuracy.Specifically,translation RMSE(root mean square error)is less than 50 mm,and rotation RMSE is less than 2◦.

关 键 词：AGV(自动导引运输车) 导航 视觉-惯性融合 多视角 二维码 全局一致 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程]