In this paper, the modified design of the currently common visual relative motion inertial navigation system (INS) is proposed to enhance the inertial measurement unit (IMU) model algorithm. The aim of the study is to improve the precise landing and indoor positioning-free flight of existing indoor drones. Both the INS and the airborne IMU used for traditional optical flow guidance are designed to input information to the same extended Kalman filter (EKF) functional module, low cost but resulting in signal phase shift and reduced reliability indicators. Unlike large drones, which can easily reduce vibration, for micro drones, the camera usually must be fixed directly to the body, making it very difficult to isolate vibrations. Using traditional methods can easily cause optical flow guidance to drag down the quality of the entire positioning function. In this study, the improved positioning guidance procedure is proposed and is applied to the indoor positioning flight of UAVs. The EKF, based on the kinematics and optical flow characteristic states equations is separated from the main navigation system. The current positioning and heading are predicted in a way where the optical flow INS and IMU are positioned separately. The positioning and heading are estimated through the current trust weighting value of the optical flow INS and the IMU. Experimental results show that the Monte Carlo method is used to simulate the impact of different signal-to-noise ratios (SNR) on acceleration, angular velocity measurement noise, and the number of detected features on the probability of success of the estimation process. At lower SNR, it shows that the proposed method can resist interference more effectively.
AbujoubSMcPheeJWestinC, et al. Unmanned aerial vehicle landing on maritime vessels using signal prediction of the ship motion, ocean. In: 2018 MTS/IEEE Charleston, Ocean2018, 2018.
2.
AsadiD.Model-based fault detection and identification of a quadrotor with rotor fault. Int J Aeronaut Space Sci2022; 23(5): 916–928.
3.
AsadiDAhmadiKNabaviSY.Fault-tolerant trajectory tracking control of a quadcopter in presence of a motor fault. Int J Aeronaut Space Sci2022; 23(1): 129–142.
4.
BergantinLRaharijaonaTRuffierF. Estimation of the distance from a surface based on local optic flow divergence. In: 2021 International conference on unmanned aircraft systems, 2021, pp.1291–1298.
5.
BorowczykANguyenDTPhu-Van NguyenA, et al. Autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle. IFAC-PapersOnLine2017; 50(1): 10488–10494.
6.
BorowczykANguyenDTNguyenAPV, et al. Autonomous landing of a quadcopter on a high-speed ground vehicle. J Guid Control Dyn2017; 40(9): 2378–2385.
7.
ChengHWChenTLTienCH.Motion estimation by hybrid optical flow technology for UAV landing in an unvisited area. Sensors2019; 19(6): 1–13.
8.
ChirarattananonP.A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and IMU measurements. Bioinspir Biomim2018; 13(3): 036004.
9.
De CroonGAlazardDIzzoD. Controlling spacecraft landings with constantly and exponentially decreasing time-to-contact. IEEE Trans Aerosp Electron Syst2015; 51(2): 1241–1252.
10.
DengJWuSZhaoH, et al. Measurement model and observability analysis for optical flow-aided inertial navigation. Opt Eng2019; 58(08): 1.
11.
FinkGFrankeMLynchAF, et al. Visual inertial SLAM: application to unmanned aerial vehicles. IFACPapersOnLine2017; 50(1): 1965–1970.
12.
GrófTBauerPWatanabeY.Positioning of aircraft relative to unknown runway with delayed image data, air data and inertial measurement fusion. Control Eng Pract2022; 125: 105211.
13.
HoHWChuQP. Automatic landing system of a quadrotor UAV using visual servoing. In: EuroGNC, 2013, pp.1264–1283.
14.
HoHWde CroonGCChuQ.Distance and velocity estimation using optical flow from a monocular camera. Int J Micro Air Veh2017; 9(3): 198–208.
15.
HoHWDe WagterCRemesBDW, et al. Optical-flow based self-supervised learning of obstacle appearance applied to MAV landing. Robot Auton Syst2018; 100(100): 78–94.
LiuWWuSWenY, et al. Integrated autonomous relative navigation method based on vision and IMU data fusion. IEEE Access2020; 8(No): 51114–51128.
18.
SrinivasanMZhangSLehrerM, et al. Honeybee navigation en route to the goal: visual flight control and odometry. J Exp Biol, No. 199, pp.237–244, 1996.
19.
TammeroLFDickinsonMH.The influence of visual landscape on the free flight behavior of the fruit fly drosophila melanogaster. J Exp Biol2003; 205(Pt 3): 327–343.
20.
McCarthyCBarnesNMahonyR.A robust docking strategy for a mobile robot using flow field divergence. IEEE Trans Robot2008; 24(4): 832–842.
21.
HerisseBRussottoFXHamelT, et al. Hovering flight and vertical landing control of a VTOL unmanned aerial vehicle using optical flow. In: IEEE/RSJ international conference on intelligent robots and systems, IROS 2008, Nice, France, 2008, pp.801–806. New York: IEEE
22.
LynenSAchtelikMWWeissS, et al. A robust and modular multi-sensor fusion approach applied to MAV navigation. In: 2013 IEEE/RSJ international conference on intelligent robots and systems, Tokyo, Japan, 2013, pp.3923–3929. New York: IEEE
23.
EngelJSturmJCremersD.Scale-aware navigation of a low-cost quadrocopter with a monocular camera. Robot Auton Syst2014; 62(11): 1646–1656.
24.
van BreugelFMorgansenKDickinsonMH. Monocular distance estimation from optic flow during active landing maneuvers. Bioinspir Biomim2014; 9(2): 2014.
25.
CorkePIGoodMC.Dynamic effects in high performance visual servoing. In: 1992 IEEE international conference on robotics and automation, Proceedings, Nice, France, 1992, pp.1838–1843. New York: IEEE.
26.
AslHJOrioloGBolandiH.An adaptive scheme for image-based visual servoing of an underactuated UAV. Int J Robot Autom2014; 29(1): 92–104.
27.
HermannRKrenerA.Nonlinear controllability and observability. IEEE Trans Automat Contr1977; 22(5): 728–740.
28.
RostenEDrummondT. Fusing points and lines for high performance tracking. In: Tenth IEEE international conference on computer vision, ICCV 2005, Beijing, China, 2005, vol. 2, pp.1508–1515. New York: IEEE.
29.
OmariSDucardG.Metric visual-qinertial navigation system using single optical flow features. In: Proceedings of the 2013 European control conference (ECC), Zurich, 2013.
30.
AchtelikMBachrachAHeR, et al. Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments. Unmanned Systems Technology XI, 2009.
31.
HerisseOBRussottoF-XHamelT, et al. Hovering flight and vertical landing control of a VTOL unmanned aerial vehicle using optical flow. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), 2008, Nice, France. New York: IEEE.
32.
GrabeVBuelthoHHGiordanoPR. On-board velocity estimation and closed-loop control of a quadrotor UAV based on optical flow. In: Proceedings of the IEEE/RSJ international conference on robotics and automation (ICRA), 2012, Saint-Paul, MN. New York: IEEE.
33.
AgrawalMKonoligeK.Rough terrain visual odometry. In: Proceedings of ICAR, 2007.
34.
BurschkaDMairE. Direct pose estimation with a monocular camera. In: Proceedings of the 2nd international conference on robot vision, RobVis, No. 08, 2008, pp.440–453, Springer-Verlag.
35.
SongXSeneviratneLDAlthoeferK.A Kalman filter-integrated optical flow method for velocity sensing of mobile robots. IEEE/ASME Trans Mechatron2011; 16(3): 551–563.
36.
RoumeliotisSIJohnsonAEMontgomeryJF.Augmenting inertial navigation with image-based motion estimation. In: Proceedings of the 2002 IEEE international conference on robotics and automation, 2002, pp.4326–4333. New York: IEEE
37.
JohnsonAE.Precise image-based motion estimation for autonomous small body exploration. In: Proceedings of the Fifth International Symposium on Artificial Intelligence, Robotics and Automation in Space, 1–3 June, 1999, pp 627–634. Noordwijk, Netherlands.
38.
HideABotterillTAndreottiM. Low cost vision aided IMU for pedestrian navigation. In: Proceedings of UPINLBS, October2010, Pp.1–7.
39.
KeQKanadeT.Transforming camera geometry to a virtual downward-looking camera: robust ego-motion estimation and ground-layer detection. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 18–20 June, 2003, Madison, WI, USA.
40.
SanchizJMPlaFMarchantJA. A framework for feature-based motion recovery in ground plane vehicle navigation. In: Proceedings of the international conference on computer analysis of images and patterns, 1997, pp.686–693. Springer-Verlag.
41.
MirzaeiFRoumeliotisS. A Kalman filter-based algorithm for imu-camera calibration. In: Proceedings of IROS, November2007, pp.2427–2434.
42.
PanahandehGJanssonM.IMU-camera self-calibration using planar mirror reflection. In: Proceedings of IPIN, 2011.
HartleyRIZissermanA.Multiple view geometry in computer vision. Cambridge: Cambridge University Press, 2000.
45.
LinHSunPCaiC, et al. Secure LQG control for a quadrotor under false data injection attacks. IET Control Theory Appl2022; 16(9): 925–934.
46.
LiangSLinHLuS, et al. Single target tracking with unknown detection status: filter design and stability analysis. IRE Trans Ind Electron2023; 71(9): 11316–11325.
