Pose Estimation with Convolutional Neural Networks
This thesis is part of a collaboration between ASU’s Interactive Robotics Laboratory and NASA’s Jet Propulsion Laboratory. In this thesis, the training pipeline from Sharma’s paper “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks” was modified to perform pose estimation on a complex object - specifically, a segment of a hollow truss. After initial attempts to replicate the architecture used in the paper and train solely on synthetic images, a combination of synthetic dataset generation and transfer learning on an ImageNet-pretrained AlexNet model was implemented to mitigate the difficulty of gathering large amounts of real-world data. Experimentation with pose estimation accuracy and hyperparameters of the model resulted in gradual test accuracy improvement, and future work is suggested to improve pose estimation for complex objects with some form of rotational symmetry.
- Author (aut): Dsouza, Susanna Roshini
- Thesis director: Ben Amor, Hani
- Committee member: Maneparambil, Kailasnath
- Contributor (ctb): Computer Science and Engineering Program
- Contributor (ctb): Computer Science and Engineering Program
- Contributor (ctb): School of Mathematical and Statistical Sciences
- Contributor (ctb): Barrett, The Honors College