In a nutshell, this project is on Pose Graph Optimization (PGO) which is typically used in most of today's SLAM Backends. The project involves:

1. Theoretical Introduction: PGO theory and 1D SLAM solved example walkthrough (redirected to Notion pages for in-depth theory).
2. Scratch: PGO Implementation from scratch on simple dataset using tools for evaluation/visualization like EVO, g2o viewer etc.
3. Using graph optimization framework G2O: PGO using G2O library on multiple datasets using tools for evaluation/visualization like EVO, g2o viewer etc.
4. PGO related survey paper reading (Optional).

Getting started:

Just fire up the Jupyter Notebook TEAM-ID_TEAM-NAME_YOUR-NAME_Project-1.ipynb for comprehensive instructions and get going, fellas!

TEAM-ID_TEAM-NAME_YOUR-NAME_Project-1.ipynb is the main notebook where you have to code and add answers.
I have added Project-1_Code-Walkthrough.ipynb for detailed instructions about compilation/libraries usage (for ex, g2o, jax, EVO). It might be daunting for beginners to get started with these libraries, so this notebook will help to a great extent.