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Hello SLAM
A course on Simultaneous Localization and Mapping

Hello SLAM

A hands-on tour through the algorithms a robot uses to figure out where it is and what the world looks like — from Bayes filters, through Kalman and particle filters, to modern graph-based SLAM.

Start with Introduction to SLAMSource notebooks

Chapters

0
1 lesson
Introduction

A short overview of Simultaneous Localization and Mapping — what it is, why we need it, and the shape of the problem.

  • 1Introduction to SLAM
1
4 lessons
Kalman Filters

Recursive Bayesian state estimation, motion and sensor models, the Kalman & Extended Kalman filters, and EKF-SLAM.

  • 1Recursive Bayes Filter
  • 2Motion & Sensor Models
  • 3Kalman & Extended Kalman Filters
  • 4EKF-SLAM
2
4 lessons
Particle Filters

Occupancy grid mapping, Monte Carlo localization, FastSLAM and Rao-Blackwellized particle filters.

  • 1Occupancy Grid Maps
  • 2Monte Carlo Localization
  • 3FastSLAM
  • 4Grid-based SLAM (RBPF)
3
3 lessons
Least Squares & Graph-based SLAM

Nonlinear least-squares estimation, the least-squares formulation of SLAM, and landmark graph-based SLAM.

  • 1Nonlinear Least Squares
  • 2Least-Squares SLAM
  • 3Landmark Graph-based SLAM