刘延旭 . 面向无人机集群的多源融合定位关键技术研究 [D ] . 北京 : 北京邮电大学 , 2022 .

LIU Y X . Research on multi-source fusion positioning towards UAV swarm [D ] . Beijing : Beijing University of Posts and Telecommunications , 2022 . (in Chinese)

陈超 . 无人集群任务分配方法研究 [D ] . 长沙 : 国防科学技术大学 , 2023 .

CHEN C . Research on task assignment algorithms in unmanned swarm [D ] . Changsha : National University of Defense Technology , 2023 . (in Chinese)

GENEVA P , ECKENHOFF K , LEE W , et al . Openvins: a research platform for visual inertial estimation [C ] // Proceedings of IEEE International Conference on Robotics and Automation.Washington,D.C.,US:IEEE , 2020 : 4666 - 4672 .

张福斌 , 张炳烁 , 杨玉帅 . 基于单目/IMU/里程计融合的SLAM算法 [J ] . 兵工学报 , 2022 , 43 ( 11 ): 2810 - 2818 .

ZHANG F B , ZHANG B S , YANG Y S . SLAM algorithm based on monocular/IMU/odometer fusion [J ] . Acta Armamentarii , 2022 , 43 ( 11 ): 2810 - 2818 . (in Chinese) DOI: 10.12382/bgxb.2022.0240 http://doi.org/10.12382/bgxb.2022.0240 It is common for navigation and positioning accuracy to be reduced when the monocular vision-inertial SLAM algorithm is applied to planar wheeled robots due to additional unobservability. To solve this problem, a tightly-coupled Visual/IMU/Odometer SLAM algorithm is proposed to improve localization accuracy. First, in the visual front-end part, the original image pyramid LK optical flow method is improved, and the rotation information of the gyroscope and the translation information from the odometer are used as priors to optimize the initial optical flow calculation process, thus reducing the calculation amount. Second, IMU/Odometer pre-integral is derived by introducing the wheel odometer information. Finally, odometer constraints are added into the initialization process and back-end nonlinear optimization to realize that vision, IMU, and odometer information are fully integrated. The results of the open-source data set test and car experiment show that the optical flow iteration time of the new algorithm is reduced by about 32.5%, and the average positioning error reduced by about 40% compared with that of VINS-Mono.

唐铭 . 基于非线性贝叶斯滤波的SLAM算法研究 [D ] . 大连 : 大连理工大学 , 2022 .

TANG M . Research on nonlinear Bayesian filtering based SLAM algorithms [D ] . Dalian : Dalian University of Technology , 2022 . (in Chinese)

范迎春 . 动态环境下的视觉SLAM地图构建研究 [D ] . 西安 : 西安电子科技大学 , 2021 .

FAN Y C . Research on visual SLAM map construction in dynamic environments [D ] . Xi’an : Xidian University , 2021 . (in Chinese)

USENKO V , DEMMEL N , SCHUBERT D , et al . Visual-inertial mapping with non-linear factor recovery [J ] . IEEE Robotics and Automation Letters , 2020 , 5 ( 2 ): 422 - 429 . DOI: 10.1109/LSP.2016. http://doi.org/10.1109/LSP.2016. https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7083369 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7083369

夏琳琳 , 张晶晶 , 初妍 , 等 . 融合天空偏振光的视觉SLAM研究进展与展望 [J/OL ] . 兵工学报 : 1 - 15 [ 2023-06-16 ] . http://kns.cnki.net/kcms/detail/11.2176.TJ.20220831.1417.002.html http://kns.cnki.net/kcms/detail/11.2176.TJ.20220831.1417.002.html http://kns.cnki.net/kcms/detail/11.2176.TJ.20220831.1417.002.html.

XIA L L , ZHANG J J , CHU Y , et al . Progresses and prospects of polarized skylight fused visual SLAM [J/OL ] . Acta Armamentarii : 1 - 15 [ 2023-06-16 ] . http://kns.cnki.net/kcms/detail/11.2176.TJ.20220831.1417.002.html http://kns.cnki.net/kcms/detail/11.2176.TJ.20220831.1417.002.html http://kns.cnki.net/kcms/detail/11.2176.TJ.20220831.1417. 002.html. (in Chinese)

MUR-ARTAL R , MONTIEL J M M , TARDOS J D . ORB-SLAM: a versatile and accurate monocular SLAM system [J ] . IEEE Transactions on Robotics , 2015 , 31 ( 5 ): 1147 - 1163 . DOI: 10.1109/TRO.2015.2463671 http://doi.org/10.1109/TRO.2015.2463671 https://ieeexplore.ieee.org/document/7219438/ https://ieeexplore.ieee.org/document/7219438/

ELVIRA R , TARDOS J D , MONTIEL J M M . ORBSLAM-atlas:a robust and accurate multi-map system:arXiv:1908.11585 [R ] . Ithaca,NY,US:Cornell University , 2019 :1908.11585.

ENGEL J , KOLTUN V , CREMERS D . Direct sparse odometry [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018 , 40 ( 3 ): 611 - 625 . DOI: 10.1109/TPAMI.2017.2658577 http://doi.org/10.1109/TPAMI.2017.2658577 Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry-represented as inverse depth in a reference frame-and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on essentially featureless walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.

LEE S H , CIVERA J . Loosely coupled semi-direct monocular SLAM [J ] . IEEE Robotics and Automation Letters , 2019 , 4 ( 2 ): 399 - 406 . DOI: 10.1109/LRA.2018.2889156 http://doi.org/10.1109/LRA.2018.2889156 https://ieeexplore.ieee.org/document/8584894/ https://ieeexplore.ieee.org/document/8584894/

BONIN-FONT F , BURGUERA A . Towards multi-robot visual graph-SLAM for autonomous marine vehicles [J ] . Journal of Marine Science and Engineering , 2020 , 8 ( 6 ): 437 . DOI: 10.3390/jmse8060437 http://doi.org/10.3390/jmse8060437 https://www.mdpi.com/2077-1312/8/6/437 https://www.mdpi.com/2077-1312/8/6/437 State of the art approaches to Multi-robot localization and mapping still present multiple issues to be improved, offering a wide range of possibilities for researchers and technology. This paper presents a new algorithm for visual Multi-robot simultaneous localization and mapping, used to join, in a common reference system, several trajectories of different robots that participate simultaneously in a common mission. One of the main problems in centralized configurations, where the leader can receive multiple data from the rest of robots, is the limited communications bandwidth that delays the data transmission and can be overloaded quickly, restricting the reactive actions. This paper presents a new approach to Multi-robot visual graph Simultaneous Localization and Mapping (SLAM) that aims to perform a joined topological map, which evolves in different directions according to the different trajectories of the different robots. The main contributions of this new strategy are centered on: (a) reducing to hashes of small dimensions the visual data to be exchanged among all agents, diminishing, in consequence, the data delivery time, (b) running two different phases of SLAM, intra- and inter-session, with their respective loop-closing tasks, with a trajectory joining action in between, with high flexibility in their combination, (c) simplifying the complete SLAM process, in concept and implementation, and addressing it to correct the trajectory of several robots, initially and continuously estimated by means of a visual odometer, and (d) executing the process online, in order to assure a successful accomplishment of the mission, with the planned trajectories and at the planned points. Primary results included in this paper show a promising performance of the algorithm in visual datasets obtained in different points on the coast of the Balearic Islands, either by divers or by an Autonomous Underwater Vehicle (AUV) equipped with cameras.

BARTOLOMEI L , KARRER M , CHLI M . Multi-robot coordination with agent-server architecture for autonomous navigation in partially unknown environments [C ] // Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington,D.C.,US:IEEE , 2020 : 1516 - 1522 .

ZOU D P , TAN P . CoSLAM: Collaborative visual SLAM in dynamic environments [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2012 , 35 ( 2 ): 354 - 366 . DOI: 10.1109/TPAMI.2012.104 http://doi.org/10.1109/TPAMI.2012.104 http://ieeexplore.ieee.org/document/6193110/ http://ieeexplore.ieee.org/document/6193110/

RIAZUELO L , CIVERA J , MONTIEL J M M . C2TAM: a cloud framework for cooperative tracking and map-ping [J ] . Robotics and Autonomous Systems , 2014 , 62 ( 4 ): 401 - 413 . DOI: 10.1016/j.robot.2013.11.007 http://doi.org/10.1016/j.robot.2013.11.007 https://linkinghub.elsevier.com/retrieve/pii/S0921889013002248 https://linkinghub.elsevier.com/retrieve/pii/S0921889013002248

FORSTER C , LYNEN S , KNEIP L , et al . Collaborative monocular SLAM with multiple micro aerial vehicles [C ] // Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington,D.C.,US:IEEE , 2013 : 3962 - 3970 .

SCHMUCK P , CHLI M . CCM-SLAM: robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams [J ] . Journal of Field Robotics , 2019 , 36 ( 4 ): 763 - 781 . DOI: 10.1002/rob.2019.36.issue-4 http://doi.org/10.1002/rob.2019.36.issue-4 https://onlinelibrary.wiley.com/toc/15564967/36/4 https://onlinelibrary.wiley.com/toc/15564967/36/4

SCHMUCK P , ZIEGLER T , KARRER M , et al . COVINS: visual-inertial SLAM for centralized collaboration [C ] // Proceedings of 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct. Washington,D.C.,US:IEEE , 2021 : 171 - 176 .

LAJOIE P Y , RAMTOULA B , CHANG Y , et al . Door-SLAM: distributed, online, and outlier resilient SLAM for robotic teams [J ] . IEEE Robotics and Automation Letters , 2020 , 5 ( 2 ): 1656 - 1663 . DOI: 10.1109/LSP.2016. http://doi.org/10.1109/LSP.2016. https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7083369 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7083369

XU H , ZHANG Y C , ZHOU B Y , et al . Omni-Swarm: a decentralized omnidirectional visual-Inertial-UWB state estimation system for aerial swarms [J ] . IEEE Transactions on Robotics , 2022 , 38 ( 6 ): 3374 - 3394 . DOI: 10.1109/TRO.2022.3182503 http://doi.org/10.1109/TRO.2022.3182503 https://ieeexplore.ieee.org/document/9813359/ https://ieeexplore.ieee.org/document/9813359/