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Cited by 123Aerospace Engineering and Control SystemsInertial Sensor and NavigationAerospace and Aviation Technology

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Advances in intelligent and autonomous navigation systems for small UAS

Bijjahalli, Suraj, Sabatini, Roberto, Gardi, Alessandro·2020·Progress in Aerospace Sciences
DOI 10.1016/j.paerosci.2020.100617
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  1. Colomina, 2014, Unmanned aerial systems for photogrammetry and remote sensing: a review, ISPRS J. Photogrammetry Remote Sens., 92, 79, 10.1016/j.isprsjprs.2014.02.013 · DOI
  2. Hassanalian, 2017, Classifications, applications, and design challenges of drones: a review, Prog. Aero. Sci., 91, 99, 10.1016/j.paerosci.2017.04.003 · DOI
  3. Kendoul, 2012, Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems, J. Field Robot., 29, 315, 10.1002/rob.20414 · DOI
  4. Jia, 2016, Obstacle detection in single images with deep neural networks, Signal, Image Video Process., 10, 1033, 10.1007/s11760-015-0855-4 · DOI
  5. Napolitano, 1995, Neural-network-based scheme for sensor failure detection, identification, and accommodation, J. Guid. Contr. Dynam., 18, 1280, 10.2514/3.21542 · DOI
  6. Sabatini, 2017, Global navigation satellite systems performance analysis and augmentation strategies in aviation, Prog. Aero. Sci., 95, 45, 10.1016/j.paerosci.2017.10.002 · DOI
  7. Wang, 2019, Impact of Terrain Factors on the Matching Performance of Terrain‐aided Navigation, Navigation, 10.1002/navi.306 · DOI
  8. Lee, 2018, A robust terrain aided navigation using the Rao-Blackwellized particle filter trained by long short-term memory networks, Sensors, 18, 2886, 10.3390/s18092886 · DOI
  9. Mondragón, 2010, Omnidirectional vision applied to Unmanned Aerial Vehicles (UAVs) attitude and heading estimation, Robot. Autonom. Syst., 58, 809, 10.1016/j.robot.2010.02.012 · DOI
  10. Scaramuzza, 2008, Appearance-guided monocular omnidirectional visual odometry for outdoor ground vehicles, IEEE Trans. Robot., 24, 1015, 10.1109/TRO.2008.2004490 · DOI · Paper page
  11. Natraj, 2013, Omnidirectional vision for UAV: applications to attitude, motion and altitude estimation for day and night conditions, J. Intell. Rob. Syst., 69, 459, 10.1007/s10846-012-9752-z · DOI
  12. Hrabar, 2009, Vision-based navigation through urban canyons, J. Field Robot., 26, 431, 10.1002/rob.20284 · DOI
  13. Ruffier, 2015, Optic flow regulation in unsteady environments: a tethered MAV achieves terrain following and targeted landing over a moving platform, J. Intell. Rob. Syst., 79, 275, 10.1007/s10846-014-0062-5 · DOI
  14. Beyeler, 2009, Vision-based control of near-obstacle flight, vol. 27, 201
  15. Garratt, 2008, Vision‐based terrain following for an unmanned rotorcraft, J. Field Robot., 25, 284, 10.1002/rob.20239 · DOI
  16. Conroy, 2009, Implementation of wide-field integration of optic flow for autonomous quadrotor navigation, Aut. Robots, 27, 189, 10.1007/s10514-009-9140-0 · DOI
  17. Chao, 2016, Flight-test evaluation of navigation information in wide-field optical flow, J. Aero. Inf. Syst., 13, 419
  18. Mejías, 2006, Visual servoing of an autonomous helicopter urban areas using feature tracking, J. Field Robot., 23, 185, 10.1002/rob.20115 · DOI
  19. Jabbari, 2014, An adaptive scheme for image-based visual servoing of an underactuated UAV, Int. J. Robot Autom., 29, 92
  20. Kanellakis, 2017, Survey on computer vision for UAVs: current developments and trends, J. Intell. Rob. Syst., 1
  21. Cappello, 2016, A low-cost and high performance navigation system for small RPAS applications, Aero. Sci. Technol., 58, 529, 10.1016/j.ast.2016.09.002 · DOI
  22. Sabatini, 2014, Navigation and guidance system architectures for small unmanned aircraft applications, Int. J. Mech. Aerospace, Indus. Mechatr. Eng., 8, 733
  23. Sabatini, 2012, Low-cost navigation and guidance systems for unmanned aerial vehicles—part 1: vision-based and integrated sensors, Annu. Navig., 19, 71, 10.2478/v10367-012-0019-3 · DOI
  24. Bhagavatula, 2011, Optic flow cues guide flight in birds, Curr. Biol., 21, 1794, 10.1016/j.cub.2011.09.009 · DOI
  25. Srinivasan, 2011, Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics, Physiol. Rev., 91, 413, 10.1152/physrev.00005.2010 · DOI
  26. Trawny, 2007, Vision‐aided inertial navigation for pin‐point landing using observations of mapped landmarks, J. Field Robot., 24, 357, 10.1002/rob.20189 · DOI
  27. Wu, 2005, Vision-aided inertial navigation for flight control, JACIC, 2, 348, 10.2514/1.16038 · DOI
  28. G. Conte and P. Doherty, "Vision-based unmanned aerial vehicle navigation using geo-referenced information," EURASIP J. Appl. Signal Process., vol. 2009, p. 10, 2009. · DOI
  29. Ryu, 2018, Vision-based wind and position estimation with fixed-wing unmanned aerial vehicle, J. Guid. Contr. Dynam., 41, 2283, 10.2514/1.G003646 · DOI
  30. Scaramuzza, 2011, Visual odometry [tutorial], IEEE Robot. Autom. Mag., 18, 80, 10.1109/MRA.2011.943233 · DOI · Paper page
  31. Engel, 2012, Accurate figure flying with a quadrocopter using onboard visual and inertial sensing, Imu, 320
  32. Kneip, 2011, Robust real-time visual odometry with a single camera and an IMU, vol. 2011
  33. Weiss, 2013, Monocular vision for long‐term micro aerial vehicle state estimation: a compendium, J. Field Robot., 30, 803, 10.1002/rob.21466 · DOI
  34. Troiani, 2015, Low computational-complexity algorithms for vision-aided inertial navigation of micro aerial vehicles, Robot. Autonom. Syst., 69, 80, 10.1016/j.robot.2014.08.006 · DOI
  35. Forster, 2017, SVO: semidirect visual odometry for monocular and multicamera systems, IEEE Trans. Robot., 33, 249, 10.1109/TRO.2016.2623335 · DOI
  36. Burri, 2016, The EuRoC micro aerial vehicle datasets, Int. J. Robot Res., 35, 1157, 10.1177/0278364915620033 · DOI
  37. Majdik, 2017, The Zurich urban micro aerial vehicle dataset, Int. J. Robot Res., 36, 269, 10.1177/0278364917702237 · DOI
  38. Achtelik, 2009, Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments, vol. 7332, 733219
  39. Warren, 2016, Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles, Int. J. Robot Res., 35, 381, 10.1177/0278364915581194 · DOI
  40. Song, 2016, A multi-sensor fusion MAV state estimation from long-range stereo, IMU, GPS and barometric sensors, Sensors, 17, 11, 10.3390/s17010011 · DOI
  41. Engel, 2017, Direct sparse odometry, IEEE Trans. Pattern Anal. Mach. Intell., 40, 611, 10.1109/TPAMI.2017.2658577 · DOI
  42. Newcombe, 2011, Kinectfusion: real-time dense surface mapping and tracking, ISMAR, 11, 127
  43. Tsai, 2017, Image-based visual servoing with light field cameras, IEEE Robot. Automat. Lett., 2, 912, 10.1109/LRA.2017.2654544 · DOI
  44. Grewal, 2010, Applications of Kalman filtering in aerospace 1960 to the present [historical perspectives], IEEE Contr. Syst. Mag., 30, 69, 10.1109/MCS.2010.936465 · DOI
  45. Higgins, 1975, A comparison of complementary and Kalman filtering, IEEE Trans. Aero. Electron. Syst., 3, 321, 10.1109/TAES.1975.308081 · DOI
  46. Mahony, 2008, Nonlinear complementary filters on the special orthogonal group, IEEE Trans. Automat. Contr., 53, 1203, 10.1109/TAC.2008.923738 · DOI
  47. S. Bonnabel, P. Martin, and P. Rouchon, "A non-linear symmetry-preserving observer for velocity-aided inertial navigation," in 2006 American Control Conference, 2006, p. 5 pp.: IEEE. · DOI
  48. Julier, 1997, New extension of the Kalman filter to nonlinear systems, AeroSense, 97, 182
  49. Julier, 2004, Unscented filtering and nonlinear estimation, Proc. IEEE, 92, 401, 10.1109/JPROC.2003.823141 · DOI
  50. Julier, 1997, New extension of the Kalman filter to nonlinear systems, vol. 3068, 182
  51. Julier, 2000, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Trans. Automat. Contr., 45, 477, 10.1109/9.847726 · DOI
  52. NøRgaard, 2000, New developments in state estimation for nonlinear systems, Automatica, 36, 1627, 10.1016/S0005-1098(00)00089-3 · DOI
  53. Rigatos, 2012, Nonlinear Kalman filters and particle filters for integrated navigation of unmanned aerial vehicles, Robot. Autonom. Syst., 60, 978, 10.1016/j.robot.2012.03.001 · DOI
  54. F. E. White, "Data fusion lexicon," Jt. Directors Labs Washington DC,1991. · DOI
  55. Llinas, 1998, An introduction to multi-sensor data fusion, vol. 6, 537
  56. Bhatti, 2007, Failure modes and models for integrated GPS/INS systems, J. Navig., 60, 327, 10.1017/S0373463307004237 · DOI
  57. Lim, 2012, Build your own quadrotor: open-source projects on unmanned aerial vehicles, IEEE Robot. Autom. Mag., 19, 33, 10.1109/MRA.2012.2205629 · DOI
  58. Li, 2019, Tight fusion of a monocular camera, MEMS-IMU, and single-frequency multi-GNSS RTK for precise navigation in GNSS-challenged environments, Rem. Sens., 11, 610, 10.3390/rs11060610 · DOI
  59. Cappello, 2018, Aircraft dynamics model augmentation for RPAS navigation and guidance, J. Intell. Rob. Syst., 91, 709, 10.1007/s10846-017-0676-5 · DOI
  60. Rhudy, 2015, Unmanned aerial vehicle navigation using wide-field optical flow and inertial sensors, J. Robot., 1
  61. Kendoul, 2009, Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles, Robot. Autonom. Syst., 57, 591, 10.1016/j.robot.2009.02.001 · DOI
  62. Song, 2011, A Kalman filter-integrated optical flow method for velocity sensing of mobile robots, IEEE ASME Trans. Mechatron., 16, 551, 10.1109/TMECH.2010.2046421 · DOI
  63. Langelaan, 2007, State estimation for autonomous flight in cluttered environments, J. Guid. Contr. Dynam., 30, 1414, 10.2514/1.27770 · DOI
  64. Diel, 2005, Epipolar constraints for vision-aided inertial navigation, vol. 2, 221
  65. Leutenegger, 2015, Keyframe-based visual–inertial odometry using nonlinear optimization, Int. J. Robot Res., 34, 314, 10.1177/0278364914554813 · DOI
  66. M. Bloesch, S. Omari, M. Hutter, and R. Siegwart, "Robust visual inertial odometry using a direct EKF-based approach," in Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, 2015, pp. 298-304: IEEE. · DOI
  67. Delmerico, 2018, A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots, Memory, 10, 20
  68. Cadena, 2016, Past, present, and future of simultaneous localization and mapping: toward the robust-perception age, IEEE Trans. Robot., 32, 1309, 10.1109/TRO.2016.2624754 · DOI · Paper page
  69. Bailey, 2006, Simultaneous localization and mapping (SLAM): Part II, IEEE Robot. Autom. Mag., 13, 108, 10.1109/MRA.2006.1678144 · DOI · Paper page
  70. Durrant-Whyte, 2006, Simultaneous localization and mapping: part I, IEEE Robot. Autom. Mag., 13, 99, 10.1109/MRA.2006.1638022 · DOI · Paper page
  71. Kim, 2004, Autonomous airborne navigation in unknown terrain environments, IEEE Trans. Aero. Electron. Syst., 40, 1031, 10.1109/TAES.2004.1337472 · DOI
  72. Kim, 2003, Airborne simultaneous localisation and map building, vol. 1, 406
  73. Montemerlo, 2002, FastSLAM: a factored solution to the simultaneous localization and mapping problem, Aaai/iaai, 593598
  74. Dissanayake, 2011, A review of recent developments in simultaneous localization and mapping, 477
  75. Hesch, 2014, Camera-IMU-based localization: observability analysis and consistency improvement, Int. J. Robot Res., 33, 182, 10.1177/0278364913509675 · DOI · Paper page
  76. Costante, 2015, Exploring representation learning with cnns for frame-to-frame ego-motion estimation, IEEE Robot. Automat Lett., 1, 18, 10.1109/LRA.2015.2505717 · DOI · Paper page
  77. Wahlström, 2015, Learning deep dynamical models from image pixels, IFAC-PapersOnLine, 48, 1059, 10.1016/j.ifacol.2015.12.271 · DOI
  78. Treml, 2016, Speeding up semantic segmentation for autonomous driving, MLITS, NIPS Workshop, 2, 7
  79. Kim, 2014, High integrity GNSS navigation and safe separation distance to support local-area UAV networks, vol. 1, 869
  80. Yang, 2018, Feature matching error analysis and modeling for consistent estimation in vision‐aided navigation, Navigation: J. Inst. Navig., 65, 609, 10.1002/navi.265 · DOI
  81. Veth, 2013, Effects of non-Gaussian and non-white noise processes on image-based targeting for mission-critical applications, vol. 3, 1988
  82. Bhatti, 2007, Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part I: a critical review, GPS Solut., 11, 173, 10.1007/s10291-006-0048-2 · DOI
  83. Bhatti, 2007, Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part II: analysis, GPS Solut., 11, 183, 10.1007/s10291-006-0049-1 · DOI
  84. Menke, 1995, Sensor/actuator failure detection in the Vista F-16 by multiple model adaptive estimation, IEEE Trans. Aero. Electron. Syst., 31, 1218, 10.1109/7.464346 · DOI
  85. Hwang, 2010, A survey of fault detection, isolation, and reconfiguration methods, IEEE Trans. Contr. Syst. Technol., 18, 636, 10.1109/TCST.2009.2026285 · DOI
  86. Samy, 2011, Survey and application of sensor fault detection and isolation schemes, Contr. Eng. Pract., 19, 658, 10.1016/j.conengprac.2011.03.002 · DOI
  87. Frank, 1990, Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: a survey and some new results, Automatica, 26, 459, 10.1016/0005-1098(90)90018-D · DOI · Paper page
  88. Sabatini, 2013, A new avionics-based GNSS integrity augmentation system: Part 2 - integrity flags, J. Navig., 66, 501, 10.1017/S0373463313000143 · DOI
  89. Sabatini, 2013, A new avionics-based GNSS integrity augmentation system: Part 1 - Fundamentals, J. Navig., 66, 363, 10.1017/S0373463313000027 · DOI
  90. Sabatini, 2014, Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid, vol. 4, 3600
  91. Bhatti, 2012, Performance of rate detector algorithms for an integrated GPS/INS system in the presence of slowly growing error, GPS Solut., 16, 293, 10.1007/s10291-011-0231-y · DOI
  92. Wang, 2016, Chi-square and SPRT combined fault detection for multisensor navigation, IEEE Trans. Aero. Electron. Syst., 52, 1352, 10.1109/TAES.2016.140860 · DOI
  93. Abuhashim, 2010, Building a robust integrity monitoring algorithm for a low cost GPS-aided-INS system, Int. J. Contr. Autom. Syst., 8, 1108, 10.1007/s12555-010-0520-1 · DOI
  94. Hide, 2003, Adaptive Kalman filtering for low-cost INS/GPS, J. Navig., 56, 143, 10.1017/S0373463302002151 · DOI
  95. Caron, 2006, GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects, Inf. Fusion, 7, 221, 10.1016/j.inffus.2004.07.002 · DOI
  96. Jwo, 2009, Navigation integration using the fuzzy strong tracking unscented Kalman filter, J. Navig., 62, 303, 10.1017/S037346330800516X · DOI
  97. Kobayashi, 1998, Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique, IEEE Trans. Ind. Electron., 45, 510, 10.1109/41.679010 · DOI
  98. Jwo, 2004, Neural network aided adaptive extended Kalman filtering approach for DGPS positioning, J. Navig., 57, 449, 10.1017/S0373463304002814 · DOI
  99. Chiang, 2003, Multisensor integration using neuron computing for land-vehicle navigation, GPS Solut., 6, 209, 10.1007/s10291-002-0024-4 · DOI
  100. Chiang, 2008, Constructive neural-networks-based MEMS/GPS integration scheme, IEEE Trans. Aero. Electron. Syst., 44, 582, 10.1109/TAES.2008.4560208 · DOI
  101. Hiliuta, 2004, Fuzzy corrections in a GPS/INS hybrid navigation system, IEEE Trans. Aero. Electron. Syst., 40, 591, 10.1109/TAES.2004.1310007 · DOI
  102. Abdel-Hamid, 2006, Improvement of MEMS-IMU/GPS performance using fuzzy modeling, GPS Solut., 10, 1, 10.1007/s10291-005-0146-6 · DOI
  103. Wilson, 2016, Vision‐aided guidance and navigation for close formation flight, J. Field Robot., 33, 661, 10.1002/rob.21637 · DOI
  104. Khansari-Zadeh, 2011, Vision-based navigation in autonomous close proximity operations using neural networks, IEEE Trans. Aero. Electron. Syst., 47, 864, 10.1109/TAES.2011.5751231 · DOI
  105. Yin, 2016, Robust visual detection–learning–tracking framework for autonomous aerial refueling of UAVs, IEEE Trans. Instrument. Meas., 65, 510, 10.1109/TIM.2015.2509318 · DOI
  106. Ciarfuglia, 2014, Evaluation of non-geometric methods for visual odometry, Robot. Autonom. Syst., 62, 1717, 10.1016/j.robot.2014.08.001 · DOI
  107. Costante, 2018, LS-VO: Learning dense optical subspace for robust visual odometry estimation, IEEE Robot. Automat. Lett., 3, 1735, 10.1109/LRA.2018.2803211 · DOI
  108. Wang, 2018, End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks, Int. J. Robot Res., 37, 513, 10.1177/0278364917734298 · DOI
  109. Bu, 2017, Integrated method for the UAV navigation sensor anomaly detection, IET Radar, Sonar Navig., 11, 847, 10.1049/iet-rsn.2016.0427 · DOI
  110. Sun, 2017, A novel online data-driven algorithm for detecting UAV navigation sensor faults, Sensors, 17, 2243, 10.3390/s17102243 · DOI
  111. Drawil, 2013, GPS localization accuracy classification: a context-based approach, IEEE Trans. Intell. Transport. Syst., 14, 262, 10.1109/TITS.2012.2213815 · DOI
  112. Trujillo, 2006, Using evolution to learn how to perform interest point detection, vol. 1, 211
  113. Mancini, 2017, Toward domain independence for learning-based monocular depth estimation, IEEE Robot. Automat. Lett., 2, 1778, 10.1109/LRA.2017.2657002 · DOI
  114. Yu, 2018, Deep learning for vision-based micro aerial vehicle autonomous landing, Int. J. Micro Air Veh., 10, 171, 10.1177/1756829318757470 · DOI
  115. Shen, 2013, A vision-based automatic safe landing-site detection system, IEEE Trans. Aero. Electron. Syst., 49, 294, 10.1109/TAES.2013.6404104 · DOI
  116. Ayhan, 2018, Semi-automated emergency landing site selection approach for UAVs, IEEE Trans. Aero. Electron. Syst., 55, 1892, 10.1109/TAES.2018.2879529 · DOI
  117. Giusti, 2016, A machine learning approach to visual perception of forest trails for mobile robots, IEEE Robot. Automat. Lett., 1, 661, 10.1109/LRA.2015.2509024 · DOI · Paper page
  118. Loquercio, 2018, Dronet: learning to fly by driving, IEEE Robot. Automat. Lett., 3, 1088, 10.1109/LRA.2018.2795643 · DOI
  119. Julian, 2019, Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning, J. Guid. Contr. Dynam., 1
  120. Julian, 2018, Deep neural network compression for aircraft collision avoidance systems, J. Guid. Contr. Dynam., 42, 598, 10.2514/1.G003724 · DOI
  121. El-Sheimy, 2006, The utilization of artificial neural networks for multisensor system integration in navigation and positioning instruments, IEEE Trans. Instrument. Meas., 55, 1606, 10.1109/TIM.2006.881033 · DOI
  122. Abdel-Hamid, 2007, Adaptive fuzzy prediction of low-cost inertial-based positioning errors, IEEE Trans. Fuzzy Syst., 15, 519, 10.1109/TFUZZ.2006.889936 · DOI
  123. Sharaf, 2005, Online INS/GPS integration with a radial basis function neural network, IEEE Aero. Electron. Syst. Mag., 20, 8, 10.1109/MAES.2005.1412121 · DOI
  124. Zhong, 2017, Approach for detecting soft faults in GPS/INS integrated navigation based on LS-SVM and AIME, J. Navig., 70, 561, 10.1017/S037346331600076X · DOI
  125. Xu, 2010, Novel hybrid of LS-SVM and Kalman filter for GPS/INS integration, J. Navig., 63, 289, 10.1017/S0373463309990361 · DOI
  126. Noureldin, 2011, GPS/INS integration utilizing dynamic neural networks for vehicular navigation, Inf. Fusion, 12, 48, 10.1016/j.inffus.2010.01.003 · DOI
  127. Yozevitch, 2016, A robust GNSS los/nlos signal classifier, Navigation: J. Inst. Navig., 63, 429, 10.1002/navi.166 · DOI
  128. Brunner, 2013, Selective combination of visual and thermal imaging for resilient localization in adverse conditions: day and night, smoke and fire, J. Field Robot., 30, 641, 10.1002/rob.21464 · DOI
  129. R. Sun, L.-T. Hsu, D. Xue, G. Zhang, and W. Y. Ochieng, "GPS signal reception classification using adaptive neuro-fuzzy inference system," J. Navig., pp. 1-17.
  130. Taylor, 2005, Rule extraction as a formal method for the verification and validation of neural networks, vol. 5, 2915
  131. Montavon, 2018, Methods for interpreting and understanding deep neural networks, Digit. Signal Process., 73, 1, 10.1016/j.dsp.2017.10.011 · DOI · Paper page
  132. Warren, 2016, Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles, Int. J. Robot Res., 35, 381, 10.1177/0278364915581194 · DOI

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Metadata

Authors
Bijjahalli, S., Sabatini, R., & Gardi, A.
Affiliations
RMIT University (AU)
Corresponding author
Sabatini, Roberto
ORCID
Venue
Progress in Aerospace Sciences
Publisher
Elsevier BV
Published
2020-05
Volume
115
Pages
100617
ISSN
0376-0421 (ISSN-L)
DOI
10.1016/j.paerosci.2020.100617
OpenAlex
W3038460818
Cited by
123
References
19
Citations by year
2026: 13 · 2025: 42 · 2024: 24 · 2023: 20 · 2022: 16 · 2021: 7 · 2020: 1
FWCI
6.41
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Progress in Aerospace Sciences (publishedVersion)
  • RMIT Research Repository (RMIT University Library) (submittedVersion)
  • Figshare (submittedVersion)
Funders
Defence Science Institute
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
  • Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
  • Physical Sciences › Engineering › Aerospace Engineering › Aerospace and Aviation Technology
Keywords
small unmanned aircraft systems, autonomous navigation, artificial neural networks, support vector machine, ensemble methods, sensor fusion, GNSS, MEMS inertial measurement unit, obstacle detection and avoidance, UAS traffic management, system certification

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