基于联合语义约束模型的多机器人协同定位

doi:10.12066/j.issn.1007-2861.2525

上海大学学报(自然科学版) ›› 2026, Vol. 32 ›› Issue (2): 212-225.doi: 10.12066/j.issn.1007-2861.2525

• 通信与信息工程 • 上一篇

基于联合语义约束模型的多机器人协同定位

方浩睿^1,2, 张金艺^1,2, 姜玉稀³

1. 上海大学特种光纤与光接入网重点实验室, 上海 200444;
2. 上海大学特种光纤与先进通信国际合作联合实验室, 上海 200444;
3. 上海三思系统集成研究所, 上海 201100

收稿日期:2023-04-30 发布日期:2026-05-11
通讯作者: 张金艺(1965-), 男, 研究员, 博士生导师, 博士, 研究方向为通信类SoC设计与室内无线定位技术. E-mail:zhangjinyi@shu.edu.cn
基金资助:
十三五国家重点研发计划资助项目(2017YFB0403500); 高等学校学科创新引智计划(111) 资助项目(D20031); 上海市闵行区重大产业技术攻关计划资助项目(2022MH-ZD19)

Multirobot collaborative location based on joint semantic constraint model

FANG Haorui^1,2, ZHANG Jinyi^1,2, JIANG Yuxi³

1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;
2. Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China;
3. Shanghai Sansi Institute for System Integration, Shanghai 201100, China

Received:2023-04-30 Published:2026-05-11

摘要/Abstract

摘要： 利用图像语义包含更多稳定环境信息的特性,提出了一种基于联合语义约束模型的多机器人协同定位算法.该算法基于一种快速旋转的二进制独立稳定描述子特征(oriented featuresfrom accelerated segment test (FAST) and rotated binary robust independentelementary feature (BRIEF),ORB),提取算法和语义分割网络,获得场景中的语义地图点,并利用该语义地图点构造语义误差函数;结合语义误差和几何重投影误差构建联合语义约束模型.在此基础上,利用地图点的语义标签结合特征词袋(bag of words,BOW)技术实现多机器人相对位姿估计,并依靠相对位姿统一多机器人位姿轨迹.最后,结合联合语义约束模型优化全局位姿,实现多机器人协同定位.结果显示,本算法在慕尼黑工业大学公共数据集的机器人同步定位与建图类别中,相比目前主流的多机器人协同定位算法,绝对位姿误差(absolute poseerror,APE)值降低了17.4%,定位精度明显提升.这证明了本算法在多机器人协同工作场景下具有良好的应用价值.

关键词: 协同定位, 语义地图点, 语义约束, 多机器人系统

Abstract: This paper proposed a multirobot collaborative location algorithm based on a joint semantic constraint model using the characteristics of image semantics, which contained stable environment information. Aided by a semantic segmentation network and the ORB (oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary feature (BRIEF)) extraction algorithm, the proposed algorithm obtained the semantic map points of scenes, used the semantic map points to construct the semantic error function, and constructed a joint semantic constraint model by combining semantic and geometric reprojection errors. Subsequently, the semantic label of feature points combined with feature bag of words (BOW) technology was used to estimate the relative pose of multiple robots, and the multirobot pose trajectory was unified based on the relative pose. Finally, the global pose was optimized by combining the joint semantic constraint model to realize multirobot collaborative localization. Experimental results showed that compared with the current mainstream multirobot collaborative localization algorithm，the proposed algorithm reduced the absolute pose error (APE) by 17.4%, thus demonstrating the applicability of the proposed algorithm to multirobot collaborative scenes.

Key words: collaborative location, semantic map point, semantic constraint, multirobot system

中图分类号:

TP751.1

方浩睿, 张金艺, 姜玉稀. 基于联合语义约束模型的多机器人协同定位[J]. 上海大学学报(自然科学版), 2026, 32(2): 212-225.

FANG Haorui, ZHANG Jinyi, JIANG Yuxi. Multirobot collaborative location based on joint semantic constraint model[J]. Journal of Shanghai University（Natural Science Edition）, 2026, 32(2): 212-225.

参考文献

[1] 陶永, 刘海涛, 王田苗, 等. 我国服务机器人技术研究进展与产业化发展趋势[J]. 机械工程学报, 2022, 58(18): 56-74.
[2] Parker L E. Current research in multirobot systems [J]. Artiflcial Life and Robotics, 2003, 7(1): 1-5.
[3] Ma T, Zhang T, Li S. Multi-robot collaborative SLAM and scene reconstruction based on RGB-D camera [C]// 2020 Chinese Automation Congress (CAC). 2020: 139-144.
[4] Mur-Artal R, Tardos J D. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras [J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262.
[5] Hesch J A, Kottas D G, Bowman S L, et al. Consistency analysis and improvement of vision-aided inertial navigation [J]. IEEE Transactions on Robotics, 2013, 30(1): 158-176.
[6] Kottas D G, Roumeliotis S I. E-cient and consistent vision-aided inertial navigation using line observations [C]// 2013 IEEE International Conference on Robotics and Automation. 2013: 1540-1547.
[7] Henry P, Krainin M, Herbst E, et al. RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments [J]. The International Journal of Robotics Research, 2012, 31(5): 647-663.
[8] Bescos B, Fácil J M, Civera J, et al. Dyna-SLAM: tracking, mapping, and inpainting in dynamic scenes [J]. IEEE Robotics and Automation Letters, 2018, 3(4): 4076-4083.
[9] 方哲, 张金艺, 姜玉稀. MR中融合语义特征传播模型的前景对象感知定位算法[J]. 上海大学学报(自然科学版), 2023, 29(1): 41-55.
[10] Yue Y, Zhao C, Li R, et al. A hierarchical framework for collaborative probabilistic semantic mapping [C]// 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020: 9659-9665.
[11] Rublee E, Rabaud V, Konolige K, et al. ORB: an e-cient alternative to SIFT or SURF [C]// 2011 IEEE International Conference on Computer Vision (ICCV). 2011: 2564-2571.
[12] He K, Gkioxari G, Dollar P, et al. Mask R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision. 2017: 2961-2969.
[13] Sturm J, Engelhard N, Endres F, et al. A benchmark for the evaluation of RGB-D SLAM systems [C]// 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2012: 573-580.

基于联合语义约束模型的多机器人协同定位

Multirobot collaborative location based on joint semantic constraint model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价