/Pose-Graph-Optimization

Optimizing the 2D trajectory of a robot from scratch using the Levenberg-Marquardt method for non-linear least squares.

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Pose-Graph-Optimization

Optimizing the 2D trajectory of a robot from scratch using the Levenberg-Marquardt method for non-linear least squares.

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).