ICUAS'17 Paper Abstract

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Paper ThA5.3

Bai, He (Oklahoma State University), Taylor, Clark (Air Force Research Lab)

Control-Enabled Observability in Visual-Inertial Odometry

Scheduled for presentation during the "Sensor Fusion - I" (ThA5), Thursday, June 15, 2017, 10:40−11:00, San Marco Island

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords Sensor Fusion, Autonomy, Navigation

Abstract

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 and the measurement equation in the line of sight coordinates, we prove observability properties for several VIO scenarios, including constant acceleration with no rotation and biased acceleration measurements. We next consider how the acceleration magnitude impacts the estimation performance. Using a planar example and Monte-Carlo simulations, we demonstrate that the estimation accuracy improves as the acceleration magnitude increases. We also show an interesting fact that deceleration along the velocity direction yields better performance than acceleration with the same magnitude for the same amount of time.

 

 

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