Deep Learning Based Object Attitude Estimation for a Laser Beam Control Research Testbed

Abstract

ABSTRACT This paper presents an object attitude estimation method using a 2D object image for a Laser Beam Control Research Testbed (LBCRT). Motivated by emerging Deep Learning (DL) techniques, a DL model that can estimate the attitude of a rotating object represented by Euler angles is developed. Instead of synthetic data for training and validation of the model, customized data is experimentally created using the laboratory testbed developed at the Naval Postgraduate School. The data consists of Short Wave Infra-Red (SWIR) images of a 3D-printed Unmanned Aerial Vehicle (UAV) model with varying attitudes and associated Euler angle labels. In the testbed, the estimated attitude is used to aim a laser beam to a specific point of the rotating model UAV object. The attitude estimation model is trained with 1684 UAV images and validated with 421 UAV images not used in the model training. The validation results show the Root-Mean-Square (RMS) angle estimation errors of 6.51 degrees in pitch, 2.74 degrees in roll, and 2.51 degrees in yaw. The Extended Kalman Filter (EKF) is also integrated to show the reduced RMS estimation errors of 1.36 degrees in pitch, 1.20 degrees in roll, and 1.52 degrees in yaw.