Vision based navigation for micro helicopters

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RotorS—A Modular Gazebo MAV Simulator Framework (2016, methodology): “For the derivations and an observability analysis, we refer to [1, 11].”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “This follows the work of Weiss (2012), who has been a close collaborator during the course of this work. While Weiss (2012) focused on visual localization, and theoretical analysis of the state estimation framework developed, this dissertation puts”
Keyframe-based visual–inertial odometry using nonlinear optimization (2015, background): “There is substantial work on the observability properties given a particular combination of sensors or measurements (Martinelli, 2011; Weiss, 2012) or only using data from a reduced set of IMU axes (Martinelli, 2014).”
Robust state estimation for Micro Aerial Vehicles based on system dynamics (2015, methodology): “This is done by expressing the true state x by our best estimate x̂ and the error state ∆x ∆x = x− x̂ (20) Calculating the Jacobians for the covariance propagation follows the derivations of Weiss [16].”
A robust and modular multi-sensor fusion approach applied to MAV navigation (2013, methodology): “In [16], we discussed the un-observability of states such as the relative position and yaw between the SLAM-frame and the world-frame in a visual-inertial navigation system.”