赵春晖1,2, 刘安萌1,2, 吕洋1,2(), 潘泉1,2
收稿日期:
2023-04-07
修回日期:
2023-05-17
接受日期:
2023-06-28
出版日期:
2024-04-25
发布日期:
2023-07-07
通讯作者:
吕洋
E-mail:lyu.yang@nwpu.edu.cn
基金资助:
Chunhui ZHAO1,2, Anmeng LIU1,2, Yang LYU1,2(), Quan PAN1,2
Received:
2023-04-07
Revised:
2023-05-17
Accepted:
2023-06-28
Online:
2024-04-25
Published:
2023-07-07
Contact:
Yang LYU
E-mail:lyu.yang@nwpu.edu.cn
Supported by:
摘要:
当前无人机(UAV)自主定位技术研究多针对特定硬件配置平台在稀疏友好环境中满足简单任务时的定位要求,在大范围复杂稠密环境和长周期复杂任务时不具备持续性、高可靠性和强适应性,制约了无人机更大规模和更广范围的应用。本文聚焦无人机韧性自主定位技术,从自主定位系统回路中的感知、估计、控制3个核心环节出发,关注持续性、可靠性和适应性等韧性指标,按多源冗余信息融合、鲁棒后端估计和具备感知意识的控制策略对国内外研究工作进行了梳理评述,指出在韧性指标要求下当前无人机自主定位技术的局限性,以及在有限机载资源条件下进行方法集成的技术难点,对无人机韧性自主定位技术的发展方向进行了展望。
中图分类号:
赵春晖, 刘安萌, 吕洋, 潘泉. 无人机韧性自主定位技术综述[J]. 航空学报, 2024, 45(8): 28839-028839.
Chunhui ZHAO, Anmeng LIU, Yang LYU, Quan PAN. A survey of resilient self-localization for UAV[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(8): 28839-028839.
表 5
后端估计过程中可能出现的误差、原因及常规解决方法
问题 | 出现原因 | 解决思路 |
---|---|---|
离群点 | 特征错误匹配,导致 优化函数专注于处理 错误数据 | 1) 通过距离筛选,如 马氏距离叠加卡方 分布作为阈值 2) 更换带阈值的 误差函数(鲁棒核 函数),如Huber核函数 3) 优化特征匹配算法 |
线性化误差 | 对非线性的观测函数和 运动函数进行线性化时, 一阶泰勒展开近似 产生误差 | 1) 逆深度参数化 2) 延迟线性化 |
一致性问题 | 线性化过程中计算 导致可观性矩阵 与实际不符 | 1) First Estimate Jacobians 2) 随机克隆 |
边缘化问题 | 由于后端不能处理所有 路标点和状态,因此 需要将部分状态固定不再考虑,这会带来一些误差 | 1) 设计不同的取舍 策略,实现边缘化的 最小影响 2) 设计更加有效的 计算链路,以更大的 算力减小边缘化程度 |
1 | 赵春晖, 胡劲文, 吕洋, 等. 无人机空域感知与碰撞规避技术[M]. 西安: 西北工业大学出版社, 2019: 20-25. |
ZHAO C H, HU J W, LYU Y, et al. UAV sense and avoid technology[M]. Xi’an: Northwestern Polytechnical University Press, 2019: 20-25 (in Chinese). | |
2 | DARPA. Fast lightweight autonomy[EB/OL]. (2017-10-13) [2023-04-07]. . |
3 | DARPA. DARPA subterranean (SubT) challenge[EB/OL]. (2017-08-07) [2023-04-07]. |
4 | ALEXIS K. Towards a science of resilient robotic autonomy[DB/OL]. arXiv preprint: 2004.02403, 2020. |
5 | SANTAMARIA-NAVARRO A, THAKKER R, FAN D D, et al. Towards resilient autonomous navigation of drones[C]∥The International Symposium of Robotics Research. Cham: Springer Cham, 2022: 922-937. |
6 | DESOUZA G N, KAK A C. Vision for mobile robot navigation: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(2): 237-267. |
7 | 秦永元, 张洪钺, 汪叔华. 卡尔曼滤波与组合导航原理[M]. 3版. 西安: 西北工业大学出版社, 2015: 287-288. |
QIN Y Y, ZHANG H Y, WANG S H. Kalman filter and integrated navigation principle[M]. 3rd ed. Xi’an: Northwestern Polytechnical University Press, 2015: 287-288 (in Chinese). | |
8 | QI H H, MOORE J B. Direct Kalman filtering approach for GPS/INS integration[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(2): 687-693. |
9 | ZHAO C H, WANG R Z, ZHANG T W, et al. Visual odometry and scene matching integrated navigation system in UAV[C]∥17th International Conference on Information Fusion (FUSION). Piscataway: IEEE Press, 2014: 1-6. |
10 | SHAN T X, ENGLOT B, MEYERS D, et al. LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping[C]∥2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: ACM, 2020: 5135–5142. |
11 | MUR-ARTAL R, TARDÓS J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803. |
12 | ROZENBERSZKI D, MAJDIK A L. LOL: Lidar-only odometry and localization in 3D point cloud maps[C]∥2020 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2020: 4379-4385. |
13 | REN K, DING L, WAN M J, et al. Target localization based on cross-view matching between UAV and satellite[J]. Chinese Journal of Aeronautics, 2022, 35(9): 333-341. |
14 | CARVALHO H, DEL MORAL P, MONIN A, et al. Optimal nonlinear filtering in GPS/INS integration[J]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(3): 835-850. |
15 | LI J X, BI Y C, LI K, et al. Accurate 3D localization for MAV swarms by UWB and IMU fusion[C]∥2018 IEEE 14th International Conference on Control and Automation (ICCA). Piscataway: IEEE Press, 2018: 100-105. |
16 | MUELLER M W, HAMER M, D’ANDREA R. Fusing ultra-wideband range measurements with accelerometers and rate gyroscopes for quadrocopter state estimation[C]∥2015 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2015: 1730-1736. |
17 | DAVISON A J, REID I D, MOLTON N D, et al. MonoSLAM: Real-time single camera SLAM[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 1052-1067. |
18 | FORSTER C, ZHANG Z C, GASSNER M, et al. SVO: Semidirect visual odometry for monocular and multicamera systems[J]. IEEE Transactions on Robotics, 2017, 33(2): 249-265. |
19 | QIN T, LI P L, SHEN S J. VINS-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020. |
20 | ECKENHOFF K, GENEVA P, HUANG G Q. MIMC-VINS: A versatile and resilient multi-IMU multi-camera visual-inertial navigation system[J]. IEEE Transactions on Robotics, 2021, 37(5): 1360-1380. |
21 | WANG C, ZHANG H D, NGUYEN T M, et al. Ultra-wideband aided fast localization and mapping system[C]∥2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2017: 1602-1609. |
22 | EBADI K, PALIERI M, WOOD S, et al. DARE-SLAM: Degeneracy-aware and resilient loop closing in perceptually-degraded environments[J]. Journal of Intelligent & Robotic Systems, 2021, 102(1): 2. |
23 | BURRI M, NIKOLIC J, GOHL P, et al. The EuRoC micro aerial vehicle datasets[J]. International Journal of Robotics Research, 2016, 35(10): 1157-1163. |
24 | ENGEL J, KOLTUN V, CREMERS D. Direct sparse odometry[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 611-625. |
25 | CAMPOS C, ELVIRA R, RODRÍGUEZ J J G, et al. ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM[J]. IEEE Transactions on Robotics, 2021, 37(6): 1874-1890. |
26 | LEUTENEGGER S, LYNEN S, BOSSE M, et al. Keyframe-based visual–inertial odometry using nonlinear optimization[J]. International Journal of Robotics Research, 2015, 34(3): 314-334. |
27 | ZHANG J, SINGH S. LOAM: Lidar odometry and mapping in real-time[C]∥Robotics: Science and Systems Conference. Robotics: Science and Systems Foundation, 2014: 1-9. |
28 | SHAN T X, ENGLOT B. LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain[C]∥2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2018: 4758-4765. |
29 | XU W, CAI Y X, HE D J, et al. FAST-LIO2: Fast direct LiDAR-inertial odometry[J]. IEEE Transactions on Robotics, 2022, 38(4): 2053-2073. |
30 | NGUYEN T M, YUAN S H, CAO M Q, et al. MILIOM: Tightly coupled multi-input lidar-inertia odometry and mapping[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 5573-5580. |
31 | SONG Y, GUAN M Y, TAY W P, et al. UWB/LiDAR fusion for cooperative range-only SLAM[C]∥2019 International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2019: 6568-6574. |
32 | GRAETER J, WILCZYNSKI A, LAUER M. LIMO: lidar-monocular visual odometry[C]∥2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2018: 7872-7879. |
33 | SHIN Y S, PARK Y S, KIM A. DVL-SLAM: Sparse depth enhanced direct visual-LiDAR SLAM[J]. Autonomous Robots, 2020, 44(2): 115-130. |
34 | SHAN T X, ENGLOT B, RATTI C, et al. LVI-SAM: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping[C]∥2021 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2021: 5692-5698. |
35 | LIN J R, ZHANG F. R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-inertial-visual tightly-coupled state estimation and mapping package[C]∥2022 International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2022: 10672-10678. |
36 | NGUYEN T M, CAO M Q, YUAN S H, et al. VIRAL-fusion: A visual-inertial-ranging-lidar sensor fusion approach[J]. IEEE Transactions on Robotics, 2022, 38(2): 958-977. |
37 | 李家宁, 田永鸿. 神经形态视觉传感器的研究进展及应用综述[J]. 计算机学报, 2021, 44(6): 1258-1286. |
LI J N, TIAN Y H. Recent advances in neuromorphic vision sensors: A survey[J]. Chinese Journal of Computers, 2021, 44(6): 1258-1286 (in Chinese). | |
38 | ZHOU Y, GALLEGO G, SHEN S J. Event-based stereo visual odometry[J]. IEEE Transactions on Robotics, 2021, 37(5): 1433-1450. |
39 | SUN S H, CIOFFI G, DE VISSER C, et al. Autonomous quadrotor flight despite rotor failure with onboard vision sensors: Frames vs. events[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 580-587. |
40 | ZHU A Z, ATANASOV N, DANIILIDIS K. Event-based visual inertial odometry[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2017: 5816-5824. |
41 | LE GENTIL C, TSCHOPP F, ALZUGARAY I, et al. IDOL: A Framework for IMU-DVS Odometry using Lines[C]∥2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2020: 5863-5870. |
42 | 李卓一. GNSS拒止的复杂环境中无人机自主导航技术研究[D]. 西安: 西北工业大学自动化学院, 2021: 17-38. |
LI Z Y. Research on autonomous navigation technology of unmanned aerial vehicles in complex GNSS-denied environments[D]. Xi’an: School of Automation, Northwestern Polytechnical University, 2021: 17-38 (in Chinese). | |
43 | 韩国良. 无人机自主返航仿生导航方法研究[D]. 长沙: 国防科技大学, 2021: 8-31. |
HAN G L. Bionic navigation method for autonomous return of UAV[D].Changsha: National University of Defense Technology, 2021: 8-31 (in Chinese). | |
44 | HARRIS C, STEPHENS M. A combined corner and edge detector[C]∥Proceedings ofthe Alvey Vision Conference 1988. Manchester: Alvey Vision Club, 1988: 147-151. |
45 | 蔡香玉, 盛业华, 黄毅, 等. 融合Harris-Laplace算子的SURF算法与无人机影像匹配[J]. 测绘科学, 2018, 43(11): 20-26, 32. |
CAI X Y, SHENG Y H, HUANG Y, et al. A SURF algorithm combined with Harris-Laplace and UAV images matching[J]. Science of Surveying and Mapping, 2018, 43(11): 20-26, 32 (in Chinese). | |
46 | 唐永鹤, 陶华敏, 卢焕章, 等. 一种基于Harris算子的快速图像匹配算法[J]. 武汉大学学报(信息科学版), 2012, 37(4): 406-409, 414. |
TANG Y H, TAO H M, LU H Z, et al. A fast image matching algorithm based on Harris operator[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 406-409, 414 (in Chinese). | |
47 | MUR-ARTAL R, MONTIEL J M M, TARDÓS J D. ORB-SLAM: A versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5): 1147-1163. |
48 | MUR-ARTAL R, TARDÓS 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. |
49 | RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF[C]∥ 2011 International Conference on Computer Vision. Piscataway: IEEE Press, 2011: 2564-2571. |
50 | LUCAS B D, KANADE T. An iterative image registration technique with an application to stereo vision[C]∥ Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2. New York: ACM, 1981: 674–679. |
51 | 张怀捷, 马静雅, 刘浩源, 等. 视觉与惯性融合的多旋翼飞行机器人室内定位技术[J]. 航空学报, 2023, 44(5): 426964. |
ZHANG H J, MA J Y, LIU H Y, et al. Indoor positioning technology of multi-rotor flying robot based on visual-inertial fusion[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(5): 426964 (in Chinese). | |
52 | SHI J B, TOMASI. Good features to track[C]∥1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2002: 593-600. |
53 | HE Y J, ZHAO J, GUO Y, et al. PL-VIO: Tightly-coupled monocular visual-inertial odometry using point and line features[J]. Sensors, 2018, 18(4): 1159. |
54 | ROSTEN E, DRUMMOND T. Fusing points and lines for high performance tracking[C]∥Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. Piscataway: IEEE Press, 2005: 1508-1515. |
55 | GROMPONE VON GIOI R, JAKUBOWICZ J, MOREL J M, et al. LSD: A fast line segment detector with a false detection control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(4): 722-732. |
56 | LYU Y, YUAN S H, XIE L H. Structure priors aided visual-inertial navigation in building inspection tasks with auxiliary line features[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 3037-3048. |
57 | ZHENG F, TSAI G, ZHANG Z, et al. Trifo-VIO: Robust and efficient stereo visual inertial odometry using points and lines[C]∥2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: ACM, 2018: 3686–3693. |
58 | GOMEZ-OJEDA R, MORENO F A, ZUÑIGA-NOËL D, et al. PL-SLAM: A stereo SLAM system through the combination of points and line segments[J]. IEEE Transactions on Robotics, 2019, 35(3): 734-746. |
59 | 王婧. 城市复杂环境下无人机自主定位与测姿技术研究[C]∥第十二届中国卫星导航年会论文集——S06 时间基准与精密授时. 北京:中国卫星导航系统管理办公室学术交流中心, 2021: 102-109. |
WANG J. Research on autonomous positioning and attitude measurement technology of UAV in complex urban environment[C]∥Proceedings of the 12th China Satellite Navigation Annual Conference—S06 Time Benchmark and Precision Timing. Beijing: Academic Exchange Center, China Satellite Navigation System Management Office, 2021: 102-109. | |
60 | YANG Y L, GENEVA P, ZUO X X, et al. Tightly-coupled aided inertial navigation with point and plane features[C]∥2019 International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2019: 6094-6100. |
61 | MOURIKIS A I, ROUMELIOTIS S I. A multi-state constraint Kalman filter for vision-aided inertial navigation[C]∥Proceedings 2007 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2007: 3565-3572. |
62 | YANG Y L, HUANG G Q. Observability analysis of aided INS with heterogeneous features of points, lines, and planes[J]. IEEE Transactions on Robotics, 2019, 35(6): 1399-1418. |
63 | FU Q, WANG J L, YU H S, et al. PL-VINS: Real-time monocular visual-inertial SLAM with point and line features[DB/OL]. arXiv preprint: 2009.07462, 2020. |
64 | BLOESCH M, OMARI S, HUTTER M, et al. Robust visual inertial odometry using a direct EKF-based approach[C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg: IEEE Press, 2015: 298-304. |
65 | SILVEIRA G, MALIS E, RIVES P. An efficient direct approach to visual SLAM[J]. IEEE Transactions on Robotics, 2008, 24(5): 969-979. |
66 | CHEN C H, WANG B, LU C X, et al. A survey on deep learning for localization and mapping: Towards the age of spatial machine intelligence[DB/OL]. arXiv preprint: 2006.12567, 2020. |
67 | ÇATAL O, JANSEN W, VERBELEN T, et al. LatentSLAM: Unsupervised multi-sensor representation learning for localization and mapping[C]∥2021 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2021: 6739-6745. |
68 | COSTANTE G, MANCINI M, VALIGI P, et al. Exploring representation learning with CNNs for frame-to-frame ego-motion estimation[J]. IEEE Robotics and Automation Letters, 2016, 1(1): 18-25. |
69 | 刘欣, 吴俊娴, 张占月. 一种基于卫星图像匹配的无人机自主定位算法[J]. 航天返回与遥感, 2021, 42(2): 130-138. |
LIU X, WU J X, ZHANG Z Y. A UAV autonomous positioning algorithm based on satellite image matching[J]. Spacecraft Recovery & Remote Sensing, 2021, 42(2): 130-138 (in Chinese). | |
70 | LIANG H J, SANKET N J, FERMÜLLER C, et al. SalientDSO: Bringing attention to direct sparse odometry[J]. IEEE Transactions on Automation Science and Engineering, 2019, 16(4): 1619-1626. |
71 | PAN J T, CANTON FERRER C, MCGUINNESS K, et al. SalGAN: Visual saliency prediction with generative adversarial networks[DB/OL]. arXiv preprint: 1701.01081, 2017. |
72 | WANG S, CLARK R, WEN H K, et al. DeepVO: Towards end-to-end visual odometry with deep recurrent convolutional neural networks[C]∥2017 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2017: 2043-2050. |
73 | 蓝朝桢, 阎晓东, 崔志祥, 等. 用于无人机自主绝对定位的实时特征匹配方法[J]. 测绘科学技术学报, 2020, 37(3): 264-268, 274. |
LAN C Z, YAN X D, CUI Z X, et al. Real-time feature matching method for the autonomous absolute location of UAV[J]. Journal of Geomatics Science and Technology, 2020, 37(3): 264-268, 274 (in Chinese). | |
74 | DETONE D, MALISIEWICZ T, RABINOVICH A. SuperPoint: Self-supervised interest point detection and description[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway: IEEE Press, 2018: 337-33712. |
75 | LIANOS K N, SCHÖNBERGER J L, POLLEFEYS M, et al. VSO: Visual semantic odometry[C]∥European Conference on Computer Vision. Cham: Springer, 2018: 246-263. |
76 | LYNEN S, ACHTELIK M W, WEISS S, et al. A robust and modular multi-sensor fusion approach applied to MAV navigation[C]∥2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE Press, 2013: 3923-3929. |
77 | MOURIKIS A I, ROUMELIOTIS S I. A dual-layer estimator architecture for long-term localization[C]∥2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE Press, 2008: 1-8. |
78 | 高翔, 张涛, 刘毅, 等. 视觉SLAM十四讲: 从理论到实践[M]. 北京: 电子工业出版社, 2017: 3-18, 257-258. |
GAO X, ZHANG T, LIU Y, et al. Fourteen lectures on visual SLAM: From theory to practice[M]. Beijing: Publishing House of Electronics Industry, 2017: 3-18, 257-258 (in Chinese). | |
79 | SIBLEY G, MATTHIES L, SUKHATME G. Sliding window filter with application to planetary landing[J]. Journal of Field Robotics, 2010, 27(5): 587-608. |
80 | KOTTAS D G, HESCH J A, BOWMAN S L, et al. On the consistency of vision-aided inertial navigation[M]∥Experimental Robotics. Berlin: Springer, 2013: 303-317. |
81 | FISCHLER M A, BOLLES R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[M]∥Readings in Computer Vision. Amsterdam: Elsevier, 1987: 726-740. |
82 | SCHÖNBERGER J L, FRAHM J M. Structure-from-motion revisited[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 4104-4113. |
83 | CIVERA J, DAVISON A J, MARTÍNEZ M J M. Inverse depth parametrization for monocular SLAM[J]. IEEE Transactions on Robotics, 2008, 24(5): 932-945. |
84 | MCLAUCHLAN P. The variable state dimension filter[R]. Guildford: University of Surrey, 1999. |
85 | MAYBECK P. Stochastic models, estimation and control, vol. 1[M]. New York: Academic, 1979: 10-20. |
86 | HUANG G P, MOURIKIS A I, ROUMELIOTIS S I. A first-estimates Jacobian EKF for improving SLAM consistency[C]∥Experimental Robotics. Berlin: Springer, 2009: 373-382. |
87 | LI M Y, MOURIKIS A I. High-precision, consistent EKF-based visual-inertial odometry[J]. The International Journal of Robotics Research, 2013, 32(6): 690-711. |
88 | ROUMELIOTIS S I, BURDICK J W. Stochastic cloning: A generalized framework for processing relative state measurements[C]∥Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292). Piscataway: IEEE Press, 2002: 1788-1795. |
89 | DONG-SI T C, MOURIKIS A I. Motion tracking with fixed-lag smoothing: Algorithm and consistency analysis[C]∥2011 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2011: 5655-5662. |
90 | HUANG G P, MOURIKIS A I, ROUMELIOTIS S I. An observability-constrained sliding window filter for SLAM[C]∥2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE Press, 2011: 65-72. |
91 | NERURKAR E D, WU K J, ROUMELIOTIS S I. C-KLAM: Constrained keyframe-based localization and mapping[C]∥2014 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2014: 3638-3643. |
92 | KLEIN G, MURRAY D. Parallel tracking and mapping for small AR workspaces[C]∥2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality. Piscataway: IEEE Press, 2007: 225-234. |
93 | KAESS M, RANGANATHAN A, DELLAERT F. iSAM: Incremental smoothing and mapping[J]. IEEE Transactions on Robotics, 2008, 24(6): 1365-1378. |
94 | KAESS M, JOHANNSSON H, ROBERTS R, et al. iSAM2: Incremental smoothing and mapping using the Bayes tree[J]. International Journal of Robotics Research, 2012, 31(2): 216-235. |
95 | LATIF Y, CADENA C, NEIRA J. Robust graph SLAM back-ends: A comparative analysis[C]∥2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE Press, 2014: 2683-2690. |
96 | SÜNDERHAUF N, PROTZEL P. Switchable constraints for robust pose graph SLAM[C]∥2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE Press, 2012: 1879-1884. |
97 | AGARWAL P, TIPALDI G D, SPINELLO L, et al. Robust map optimization using dynamic covariance scaling[C]∥2013 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2013: 62-69. |
98 | OLSON E, AGARWAL P. Inference on networks of mixtures for robust robot mapping[J]. International Journal of Robotics Research, 2013, 32(7): 826-840. |
99 | LATIF Y, CADENA C, NEIRA J. Robust loop closing over time for pose graph SLAM[J]. International Journal of Robotics Research, 2013, 32(14): 1611-1626. |
100 | VASILEIOS T. Resilient submodular maximization for control and sensing[D]. Philadelphia: University of Penns⁃ylvania, 2018: 1-8. |
101 | HARSHAW C, FELDMAN M, WARD J, et al. Submodular maximization beyond non-negativity: Guarantees, fast algorithms, and applications[DB/OL]. arXiv preprint: 1904.09354, 2019. |
102 | BALLOTTA L, SCHENATO L, CARLONE L. Computation-communication trade-offs and sensor selection in real-time estimation for processing networks[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(4): 2952-2965. |
103 | JAWAID S T, SMITH S L. Submodularity and greedy algorithms in sensor scheduling for linear dynamical systems[J]. Automatica, 2015, 61: 282-288. |
104 | MOUSAVI H K, MOTEE N. Estimation with fast feature selection in robot visual navigation[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 3572-3579. |
105 | CARLONE L, KARAMAN S. Attention and anticipation in fast visual-inertial navigation[C]∥2017 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2017: 3886-3893. |
106 | KHOSOUSSI K, GIAMOU M, SUKHATME G S, et al. Reliable graphs for SLAM[J]. International Journal of Robotics Research, 2019, 38(2-3): 260-298. |
107 | CHEN Y B, HUANG S D, ZHAO L, et al. Cramér–Rao bounds and optimal design metrics for pose-graph SLAM[J]. IEEE Transactions on Robotics, 2021, 37(2): 627-641. |
108 | FALANGA D, FOEHN P, LU P, et al. PAMPC: perception-aware model predictive control for quadrotors[C]∥2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2018: 1-8. |
109 | ALMADHOUN R, ABDULDAYEM A, TAHA T, et al. Guided next best view for 3D reconstruction of large complex structures[J]. Remote Sensing, 2019, 11(20): 2440. |
110 | CAO N N, LOW K H, DOLAN J M. Multi-robot informative path planning for active sensing of environmental phenomena: A tale of two algorithms[DB/OL]. arXiv preprint: 302.0723, 2013. |
111 | ZHANG Z C, SCARAMUZZA D. Fisher information field: An efficient and differentiable map for perception-aware planning[DB/OL]. arXiv preprint: 2008.03324, 2020. |
112 | SALARIS P, COGNETTI M, SPICA R, et al. Online optimal perception-aware trajectory generation[J]. IEEE Transactions on Robotics, 2019, 35(6): 1307-1322. |
113 | OPENAI, ACHIAM J,et al. GPT-4 technical report[DB/OL]. arXiv preprint: 2303.08774, 2023. |
114 | 苏翎菲, 化永朝, 董希旺, 等. 人与无人机集群多模态智能交互方法[J]. 航空学报, 2022, 43(S1): 727001. |
SU L F, HUA Y Z, DONG X W, et al. Human-UAV swarm multi-modal intelligent interaction methods[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(S1): 727001 (in Chinese). |
[1] | 王涛, 高雪峰, 祝景萍, 董松, 孙连军, 郑侃. 机器人纵扭超声铣边颤振在线监测方法[J]. 航空学报, 2023, 44(13): 262-272. |
[2] | 文超, 董文瀚, 解武杰, 蔡鸣, 刘日. 基于回访机制的无人机集群分布式协同区域搜索方法[J]. 航空学报, 2023, 44(11): 327561-327561. |
[3] | 林京, 张博瑶, 张大义, 陈敏. 航空燃气涡轮发动机故障诊断研究现状与展望[J]. 航空学报, 2022, 43(8): 626565-626565. |
[4] | 冯立好, 魏凌云, 董磊, 王晋军. 飞翼布局飞机耦合运动失稳的主动流动控制[J]. 航空学报, 2022, 43(10): 527353-527353. |
[5] | 石健, 王少萍, 罗雪松. 基于不确定传感器状态的机载系统多层故障诊断方法[J]. 航空学报, 2021, 42(6): 624376-624376. |
[6] | 叶子鹏, 周庆瑞, 王辉. 日地L2点航天器编队的分布式自主相对导航[J]. 航空学报, 2021, 42(2): 324145-324145. |
[7] | 尹东亮, 黄晓颖, 吴艳杰, 何有宸, 谢经伟. 基于云模型和改进D-S证据理论的目标识别决策方法[J]. 航空学报, 2021, 42(12): 324768-324768. |
[8] | 王巍, 邢朝洋, 冯文帅. 自主导航技术发展现状与趋势[J]. 航空学报, 2021, 42(11): 525049-525049. |
[9] | 梁帅, 杨林, 杨朝旭, 许斌. 基于Kalman滤波的变体飞行器T-S模糊控制[J]. 航空学报, 2020, 41(S2): 724274-724274. |
[10] | 林清, 蔡志浩, 闫坤, 王英勋. 升降舵辅助操纵的自转旋翼机自适应姿态控制[J]. 航空学报, 2016, 37(9): 2820-2832. |
[11] | 王聪, 王海鹏, 熊伟, 何友. 一种基于最小二乘拟合的数据关联算法[J]. 航空学报, 2016, 37(5): 1603-1613. |
[12] | 衣晓, 韩健越, 张怀巍, 关欣. 基于区实混合序列相似度的异步不等速率航迹关联算法[J]. 航空学报, 2015, 36(4): 1212-1220. |
[13] | 刘君强, 谢吉伟, 左洪福, 张马兰. 基于随机Wiener过程的航空发动机剩余寿命预测[J]. 航空学报, 2015, 36(2): 564-574. |
[14] | 王昱, 章卫国, 傅莉, 黄得刚, 李勇. 基于改进证据网络的空战动态态势估计方法[J]. 航空学报, 2015, 36(12): 3896-3909. |
[15] | 宋伟, 朱岱寅, 叶少华, 李勇. 一种机载合成孔径雷达自主定位算法[J]. 航空学报, 2014, 35(8): 2279-2285. |
阅读次数 | ||||||||||||||||||||||||||||||||||||||||||||||||||
全文 377
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||
摘要 855
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||
版权所有 © 航空学报编辑部
版权所有 © 2011航空学报杂志社
主管单位:中国科学技术协会 主办单位:中国航空学会 北京航空航天大学