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.

Resources

• https://theairlab.org/tartanslamseries/
• https://www.mathworks.com/discovery/slam.html
• https://navigation.ros.org/tutorials/docs/navigation2_with_slam.html?highlight=slam
• A really great 2-part paper
  • https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1638022
  • https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1678144
• http://ais.informatik.uni-freiburg.de/teaching/ws11/robotics2/pdfs/ls-slam-tutorial.pdf
• Understanding SLAM Using Pose Graph Optimization | Autonomous Navigation, Part 3

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)

Concepts

• SLAM Formalization
• Graph Optimization
• Bundle Adjustment
• Pose Graph Optimization
• Loop Closure

Implementations

2D SLAM

• slam_toolbox
• Gmapping from OpenSlam
• Google Cartographer

Visual SLAM

• ORB-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)