Tracking ground targets with measurements obtained from a single monocular camera mounted on an Unmanned Aerial Vehicle

Abstract

The core objective of this research is to develop an estimator capable of tracking the states of ground targets with observation measurements obtained from a single monocular camera mounted on a small unmanned aerial vehicle (UAV). Typical sensors on a small UAV include an inertial measurement unit (IMU) with three axes accelerometer and rate gyro sensors and a global positioning system (GPS) receiver which gives position and velocity estimates of the UAV. Camera images are combined with these measurements in state estimate filters to track ground features of opportunity and a target. The images are processed by a keypoint detection and matching algorithm that returns pixel coordinates for the features. Kinematic state equations are derived that reflect the relationships between the available input and output measurements and the states of the UAV, features, and target. These equations are used in the development of coupled state estimators for the dynamic state of the UAV, for estimation of feature positions, and for estimation of target position and velocity. The estimator developed is tested in MATLAB/SIMULINK, where GPS and IMU data are generated from the simulated states of a nonlinear model of a Navion aircraft. Images are also simulated based upon a fabricated environment consisting of features and a moving ground target. Target observability limitations are overcome by constraining the target vehicle to follow ground terrain, defined by local features, and subsequent modification of the target's observation model. An unscented Kalman filter (UKF) provides the simultaneous localization and mapping solution for the estimation of aircraft states and feature locations. Another filter, a loosely coupled Kalman filter for the target states, receives 3D measurements of target position with estimated covariance obtained by an unscented transformation (UT). The UT uses the mean and covariance from the camera measurements and from the UKF estimated aircraft states and feature locations to determine the estimated target mean and covariance. Simulation results confirm that the new loosely coupled filters are capable of estimating target states. Experimental data, collected from a research UAV, explores the effectiveness of the terrain estimation techniques required for target tracking.