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Visual-inertial odometry (VIO) is a nonlinear estimation problem where control inputs, such as acceleration and angular velocity, play a significant role in the estimation performance. In this paper, we examine effects of controls on the VIO problem. We first analyze the effects of acceleration and angular velocity inputs on state observability of the VIO problem. Representing the vehicle dynamics...
Relative state estimation is an important component in multi-vehicle applications in GPS-denied environments. In this paper, we consider relative heading estimation between two Unmanned Aerial Systems (UAS). We examine observability properties of relative states between the two UAS in the presence of constant disturbances. We assume that UAS 2 measures UAS 1's position in its own frame. We first present...
Relative pose estimation between fixed-wing unmanned aerial vehicles (UAVs) is treated using a stable and robust estimation scheme. The motivating application of this scheme is that of “handoff” of an object being tracked from one fixed-wing UAV to another in a team of UAVs, using onboard sensors in a GPS-denied environment. This estimation scheme uses optical measurements from cameras onboard a vehicle,...
The RANdom SAmple Consensus (RANSAC) algorithm, as a robust parameter estimator, has been widely used to remove gross errors. However, there is less work on analyzing the uncertainty produced by the RANSAC. This paper fills this gap by presenting an uncertainty estimation algorithm for the RANSAC. Based on a thorough analysis on the uncertainty of the model parameters generated during the random hypothesis...
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