Radar and Visual Odometry Integrated System Aided Navigation for UAVS in GNSS Denied Environment

被引:50
作者
Mostafa, Mostafa [1 ]
Zahran, Shady [1 ]
Moussa, Adel [1 ,2 ]
El-Sheimy, Naser [1 ]
Sesay, Abu [3 ]
机构
[1] Univ Calgary, Dept Geomat Engn, Calgary, AB T2N 1N4, Canada
[2] Port Said Univ, Dept Elect Engn, Port Said 42523, Egypt
[3] Univ Calgary, Dept Elect & Comp Engn, Calgary, AB T2N 1N4, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
multi-sensor fusion; regression trees; GNSS; EKF; UAVs; INS; RO; VO; GPS;
D O I
10.3390/s18092776
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system's ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.
引用
收藏
页数:29
相关论文
共 32 条
[1]  
Achtelik Markus, 2011, IEEE International Conference on Robotics and Automation, P3056
[2]  
Banerjee S., 2014, LINEAR ALGEBRA MATRI
[3]   Speeded-Up Robust Features (SURF) [J].
Bay, Herbert ;
Ess, Andreas ;
Tuytelaars, Tinne ;
Van Gool, Luc .
COMPUTER VISION AND IMAGE UNDERSTANDING, 2008, 110 (03) :346-359
[4]  
Bryson M, 2009, IEEE INT CONF ROBOT, P3143
[5]  
Civera J, 2007, LECT NOTES COMPUT SC, V4478, P412
[6]   MonoSLAM: Real-time single camera SLAM [J].
Davison, Andrew J. ;
Reid, Ian D. ;
Molton, Nicholas D. ;
Stasse, Olivier .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2007, 29 (06) :1052-1067
[7]   USE OF HOUGH TRANSFORMATION TO DETECT LINES AND CURVES IN PICTURES [J].
DUDA, RO ;
HART, PE .
COMMUNICATIONS OF THE ACM, 1972, 15 (01) :11-&
[8]   UAV Altitude Estimation by Mixed Stereoscopic Vision [J].
Eynard, Damien ;
Vasseur, Pascal ;
Demonceaux, Cedric ;
Fremont, Vincent .
IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, :646-651
[9]   INACCURACIES IN DOPPLER RADAR NAVIGATION SYSTEMS DUE TO TERRAIN DIRECTIVITY EFFECTS NONZERO BEAMWIDTHS + ECLIPSING [J].
FEUERSTEIN, E ;
SAFRAN, H ;
JAMES, PN .
IEEE TRANSACTIONS ON AEROSPACE AND NAVAL ELECTRONICS, 1964, AN11 (02) :101-&
[10]  
Huang KC, 2012, IEEE INT CONF ROBOT, P2102, DOI 10.1109/ICRA.2012.6224767