Electronic Theses and Dissertations
Date
2020
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Electrical & Computer Engineering
Committee Chair
Eddie Jacobs
Committee Member
Deepak Venugopal
Committee Member
Aaron Robinson
Committee Member
Madhusudhanan Balasubramanian
Abstract
Artificial Intelligence has grown into an enormous field encompassing tasks formerly thought to require human intelligence. It encompasses the field of Machine Learning (ML) which uses data intensive learning algorithms to model complex data. Deep learning, using artificial neural networks, is the most prolific ML method. It can achieve responses to data that rival those of biological systems such as the visual cortexs response to motion. The field of computer vision has benefited from the availability of the deep learning methods throughout many common task such as object recognition, segmentation, and scene reconstruction but advancements in the area of Simultaneous Localization and Mapping (SLAM) has been gradual in comparison. The localization element of SLAM is computationally expensive and contains many years of refined domain knowledge that is not directly applicable to the neural approaches causing erroneous assumptions and error prone implementation. This is in part due to the temporal element of data associated with visual odometry. This research proposes a novel paradigm for feature engineering with respect to neural network architecture. It addresses some of the possible implementation assumptions. Internal feature engineering focuses on using a method that is internal to the neural architecture to expose the network to components of more favorable features. External feature engineering focuses on utilizing methods that operate outside of the neural architecture. The results show that these methods improve the task of iterative pose estimation for self-localization and are generally applicable for other tasks.
Library Comment
Dissertation or thesis originally submitted to ProQuest
Recommended Citation
Parkins, Franz, "Internal and External Feature Engineering applied to Deep Learning with Convolutional Neural Networks for Monocular Relative Pose Estimation in Visual Odometry and Self-Localization" (2020). Electronic Theses and Dissertations. 2705.
https://digitalcommons.memphis.edu/etd/2705
Comments
Data is provided by the student.