Name of Faculty Sponsor
Kevin McFall, PhD.
Faculty Sponsor Email
Visual odometry is the process of tracking an agent's motion over time using a visual sensor. The visual odometry problem has only been recently solved using traditional, non-machine learning techniques. Despite the success of neural networks at many related problems such as object recognition, feature detection, and optical flow, visual odometry still has not been solved with a deep learning technique. This paper attempts to implement several Convolutional Neural Networks to solve the visual odometry problem and compare slight variations in data preprocessing. The work presented is a step toward reaching a legitimate neural network solution.
Graves, Alec; Lim, Steffen; Fagan, Thomas; and McFall, Kevin PhD.
"Visual Odometry using Convolutional Neural Networks,"
The Kennesaw Journal of Undergraduate Research: Vol. 5
, Article 5.
Available at: https://digitalcommons.kennesaw.edu/kjur/vol5/iss3/5