GraphSLAM educational implementation

A bare-bones GraphSLAM implementation based on Chapter 11 of [1]. Mostly for self-educational purposes. Currently has two entry points:

• graph_slam.py can be executed as a sort of single-shot application. Minimal dependencies.
• visualizer/visualizer.py is a Qt5 + PyQt5 based interactive application, offers a richer experience.

Implemented functionality

The application can:

• Generate a simple world with point-like landmarks.
• Generate a random ego-path using a constant turn rate and velocity motion model.
• Generate landmark observations for each ego state along the path using a simple stochastic sensor model.
• Perform the algorithmic steps of GraphSLAM:
  • Initialize
  • Linearize
  • Reduce
  • Solve

[1]: Probabilistic Robotics, Thrun, S. and Burgard, W. and Fox, D. and Arkin, R.C. 2005 MIT Press

Notice

Extracted from the repository: https://github.com/Bazs/probabilistic-robotics/tree/master/ProbabilisticRoboticsPython/graph_slam. Currently only this repository receives updates.