In this project I present a novel Simulated and Unsupervised (S+U) learning approach for the generation of realistic imaging sonar datasets.
This project mainly focuses on the prerequisite of feature-based ASfM algorithm.
In this project, we focus on a large-scale optimal transportation model to perform the alignment of the representations in the source and target domains.
This project analyzed the performance of the state-of-the-art graph-based SLAM algorithms with robust kernel.
