  In this paper, we propose a visual-inertial framework able to efficiently
estimate the camera poses of a non-rigid trinocular baseline for long-range
depth estimation on-board a fast moving aerial platform. The estimation of the
time-varying baseline is based on relative inertial measurements, a photometric
relative pose optimizer, and a probabilistic wing model fused in an efficient
Extended Kalman Filter (EKF) formulation. The estimated depth measurements can
be integrated into a geo-referenced global map to render a reconstruction of
the environment useful for local replanning algorithms. Based on extensive
real-world experiments we describe the challenges and solutions for obtaining
the probabilistic wing model, reliable relative inertial measurements, and
vision-based relative pose updates and demonstrate the computational efficiency
and robustness of the overall system under challenging conditions.
