Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms
This addresses obstacle avoidance for fixed-wing UAVs, but it is incremental as it builds on existing sensor fusion and modeling techniques.
The paper tackles the problem of estimating time-varying relative pose between visual-inertial sensors on flexible-wing UAVs, resulting in highly accurate depth maps for real-time obstacle avoidance in low-altitude flights.
This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated relative poses are used to generate highly accurate depth maps in real-time and can be employed for obstacle avoidance in low-altitude flights or landing maneuvers. The approach is structured as follows: Initially, a wing model is identified by fitting a probability density function to measured deviations from the nominal relative baseline transformation. At run-time, the prior knowledge about the wing model is fused in an Extended Kalman filter~(EKF) together with relative pose measurements obtained from solving a relative perspective N-point problem (PNP), and the linear accelerations and angular velocities measured by the two inertial measurement units (IMU) which are rigidly attached to the cameras. Results obtained from extensive synthetic experiments demonstrate that our proposed framework is able to estimate highly accurate baseline transformations and depth maps.