Abstract
Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements.
Highlights
In recent years the research for autonomous unmanned aircraft systems (UAS) has focused on indoor navigation without benefit of global navigation satellite systems (GNSS)
On-board stabilization and autonomous navigation of multi-rotor UAS in GNSS-denied environments require the handling of fast flight dynamics solely based on indirect measurements
Six degrees of freedom (DOF) UAS ego-motion estimation is usually tackled by the fusion of inertial measurement units (IMU)-outputs with further measurements, e.g. from compass, barometer and ultrasonic, using extended Kalman (Haykin, 2001) or particle filtering (Ristic et al, 2004)
Summary
In recent years the research for autonomous UAS has focused on indoor navigation without benefit of global navigation satellite systems (GNSS). Six degrees of freedom (DOF) UAS ego-motion estimation is usually tackled by the fusion of IMU-outputs with further measurements, e.g. from compass, barometer and ultrasonic, using extended Kalman (Haykin, 2001) or particle filtering (Ristic et al, 2004). Pose estimation based on these sensors is known to be affected by high drift rates. SBA (Lourakis, 2010), keyframe selection (Klein and Murray, 2007), tracking of natural landmarks (Tardif et al, 2008) and loop closing techniques like simultaneous localization and mapping (SLAM) (Bailey and Durrant-Whyte, 2006; Durrant-Whyte and Bailey, 2006) are common approaches to reduce VO estimation uncertainties and drift rates
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