Simultaneous Localization and Mapping (SLAM)

SLAM is a technique for Robots to simultaneously do Localization and Mapping.

SLAM stands for “Simultaneous Localization and Mapping”. It is a computational problem in robotics and computer vision that involves creating a map of an unknown environment while at the same time locating the robot or camera within that environment.

For camera-based SLAM, see Visual SLAM.


First introduced to this idea by George Hotz through his livestream livecoding SLAM.

Approaches to SLAM:

  1. Kalman Filter based
  2. Particle Filter
  3. Graph-Based SLAM (MODERN technique)




Visual SLAM

2D SLAM Study

TODO: insert the confluence studies?

When SLAM doesn't work...

When SLAM doesn’t work for me, I always find that it is an odometry problem, because you end up with these really bad estimates of the positions…

We went through a bunch of possible explanations:

  • we were turning too sharply, and the turning was was wrong, therefore wheel slip
  • The intervals recording the points was to large (turns out this is actually a good thing)