Robust monocular relative pose measurement for carrier-based aircraft landing guidance

Robust and accurate relative pose measurement is one of the key technologies for shipboard aircraft autonomous landing. The airborne monocular pose estimation methods, based on key-points detection and Perspective-n-Points (PnP) problem solving, have drawn significant attention of researchers for its strengths in ease of deployment, power efficiency and anti-electromagnetic interference, etc. However, the inaccuracy of key-points detection is not considered in the existing methods, which corrupts the precision of the pose identification. To address this problem, a tightly-coupled AEKF-based monocular pose tracking method is proposed. The pose measurement problem is transferred into motion state estimation of the aircraft. An extended Kalman filter system is established taking the key-point detection results as observations, with the carrier represented in form of a sparse key-point set. For the unknown statistics of the observations, an adaptive noise covariance estimation method based on sliding window is put forward. Synthetic and real scaled experiments demonstrate that the proposed method achieves robust and accurate online pose tracking, superior to traditional approaches.