[1]

Hachem M, Miquel T, Bronz M, Roos C. 2025. Trajectory optimization for fully actuated hexacopters: Enhancing maneuverability and applications. International Journal of Micro Air Vehicles 17:17568293251332313

doi: 10.1177/17568293251332313
[2]

Pfeiffer S, De Wagter C, de Croon GC. 2025. Improved moving horizon estimation for ultra-wideband localization on small drones. International Journal of Micro Air Vehicles 17:17568293251340100

doi: 10.1177/17568293251340100
[3]

Qin T, Li P, Shen S. 2018. VINS-mono: a robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics 34:1004−1020

doi: 10.1109/TRO.2018.2853729
[4]

Campos C, Elvira R, Rodríguez JJG, Montiel JMM, Tardós JD. 2021. ORB-SLAM3: an accurate open-source library for visual, visual–inertial, and multimap SLAM. IEEE Transactions on Robotics 37:1874−1890

doi: 10.1109/TRO.2021.3075644
[5]

Shan T, Englot B, Meyers D, Wang W, Ratti C, et al. 2020. LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping. Proc. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24 October, 2020−24 January, 2021. USA: IEEE. pp. 5135–5142 doi: 10.1109/IROS45743.2020.9341176
[6]

Xu W, Cai Y, He D, Lin J, Zhang F. 2022. FAST-LIO2: fast direct LiDAR-inertial odometry. IEEE Transactions on Robotics 38:2053−2073

doi: 10.1109/TRO.2022.3141876
[7]

Cai Y, Xu W, Zhang F. 2021. Ikd-tree: an incremental K-D tree for robotic applications. arXiv Preprint:2102.10808

doi: 10.48550/arXiv.2102.10808
[8]

Zuo X, Geneva P, Lee W, Liu Y, Huang G. 2019. LIC-fusion: LiDAR-inertial-camera odometry. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3–8 November, 2019. USA: IEEE. pp. 5848–5854 doi: 10.1109/IROS40897.2019.8967746
[9]

Lin J, Zhang F. 2022. R3LIVE: a robust, real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state estimation and mapping package. 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 23–27 May, 2022.USA: IEEE. 10672–10678 doi: 10.1109/ICRA46639.2022.9811935
[10]

Cao S, Lu X, Shen S. 2022. GVINS: tightly coupled GNSS–visual–inertial fusion for smooth and consistent state estimation. IEEE Transactions on Robotics 38:2004−2021

doi: 10.1109/TRO.2021.3133730
[11]

Tang H, Niu X, Zhang T, Wang L, Liu J. 2023. LE-VINS: a robust solid-state-LiDAR-enhanced visual-inertial navigation system for low-speed robots. IEEE Transactions on Instrumentation and Measurement 72:8502113

doi: 10.1109/TIM.2023.3260279
[12]

Liu X, Wen W, Hsu LT. 2024. GLIO: tightly-coupled GNSS/LiDAR/IMU integration for continuous and drift-free state estimation of intelligent vehicles in urban areas. IEEE Transactions on Intelligent Vehicles 9:1412−1422

doi: 10.1109/TIV.2023.3323648
[13]

He D, Li H, Yin J. 2025. LIGO: a tightly coupled LiDAR-inertial-GNSS odometry based on a hierarchy fusion framework for global localization with real-time mapping. IEEE Transactions on Robotics 41:1224−1244

doi: 10.1109/TRO.2025.3530298
[14]

Rehder J, Nikolic J, Schneider T, Hinzmann T, Siegwart R. 2016. Extending kalibr: calibrating the extrinsics of multiple IMUs and of individual axes. 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16–21 May, 2016. USA: IEEE. pp. 4304–4311 doi: 10.1109/ICRA.2016.7487628
[15]

Zheng C, Zhu Q, Xu W, Liu X, Guo Q, et al. 2022. FAST-LIVO: fast and tightly-coupled sparse-direct LiDAR-Inertial-Visual Odometry. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23–27 October, 2022. USA: IEEE. pp. 4003–4009 doi: 10.1109/IROS47612.2022.9981107
[16]

Tang H, Zhang T, Niu X, Wang L, Wei L, et al. 2023. FF-LINS: a consistent frame-to-frame solid-state-LiDAR-inertial state estimator. IEEE Robotics and Automation Letters 8:8525−8532

doi: 10.1109/LRA.2023.3329625
[17]

Qin T, Cao S, Pan J, Shen S. 2025. A general optimisation-based framework for global pose estimation with multiple sensors. IET Cyber‐Systems and Robotics 7:e70023

doi: 10.1049/csy2.70023
[18]

Shan T, Englot B, Ratti C, Rus D. 2021. LVI-SAM: tightly-coupled lidar-visual-inertial odometry via smoothing and mapping. 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–05 June, 2021. USA: IEEE. pp. 5692–5698 doi: 10.1109/ICRA48506.2021.9561996
[19]

Mur-Artal R, Montiel JMM, Tardós JD. 2015. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics 31:1147−1163

doi: 10.1109/TRO.2015.2463671
[20]

Qin T, Shen S. 2018. Online temporal calibration for monocular visual-inertial systems. Proc. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1–5 October, 2018. USA: IEEE. pp. 3662–3669 doi: 10.1109/IROS.2018.8593603
[21]

Xu W, Zhang F. 2021. FAST-LIO: a fast, robust LiDAR-inertial odometry package by tightly-coupled iterated Kalman filter. IEEE Robotics and Automation Letters 6:3317−3324

doi: 10.1109/LRA.2021.3064227
[22]

Niu X, Tang H, Zhang T, Fan J, Liu J. 2023. IC-GVINS: a robust, real-time, INS-centric GNSS-visual-inertial navigation system. IEEE Robotics and Automation Letters 8:216−223

doi: 10.1109/LRA.2022.3224367
[23]

Tang H, Zhang T, Wang L, Ding X, Yuan M, et al. 2025. i2Nav-robot: a large-scale indoor-outdoor robot dataset for multi-sensor fusion navigation and mapping. arXiv Preprint:2508.11485

doi: 10.48550/arXiv.2508.11485
[24]

Nguyen TM, Yuan S, Cao M, Lyu Y, Nguyen TH, et al. 2022. NTU VIRAL: a visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint. The International Journal of Robotics Research 41:270−280

doi: 10.1177/02783649211052312