Enhanced Image-Aided Navigation Algorithm with Automatic Calibration and Affine Distortion Prediction
Date of Award
Master of Science
Department of Electrical and Computer Engineering
Kenneth A. Fisher, PhD.
This research aims at improving two key steps within the image aided navigation process: camera calibration and landmark tracking. The camera calibration step is improved by automating the point correspondence calculation within the standard camera calibration algorithm, thereby reducing the required time for calibration while maintaining the output model accuracy. The feature landmark tracking step is improved by digitally simulating affine distortions on input images in order to calculate more accurate feature descriptors for improved feature matching in high relative viewpoint change. These techniques are experimentally demonstrated in an outdoor environment with a consumer-grade inertial sensor and three imaging sensors, one of which is orthogonal to the rest. Using a tactical-grade inertial sensor coupled with GPS position data for comparison, the improved image aided navigation algorithm is shown to reduce navigation errors by 24% in position, 16% in velocity and 35% in attitude when compared to the standard image-aided navigation algorithm.
DTIC Accession Number
Jurado, Juan D., "Enhanced Image-Aided Navigation Algorithm with Automatic Calibration and Affine Distortion Prediction" (2012). Theses and Dissertations. 1123.
Juan D. Jurado is a 2022 recipient of the AFIT Young Alumni Award, for achievements of alumni who have made outstanding contributions in their career within ten years of graduating from AFIT.