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.

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

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

More details and resources in Visual SLAM.

Two main implementations

1. EKF-SLAM (for Online)
   • Use Scan Matching
2. Graph-Based SLAM (for Offline)
   • Full trajectories are estimated using a complete set of measurements

Main Steps from ChatGPT

1. Data collection
2. Data association -> Scan Matching
3. State Estimation and State Update

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)

Implementation §

2D SLAM

• slam_toolbox
• Gmapping from OpenSlam
• Google Cartographer

Visual SLAM

• ORB-SLAM

Loop Closure §

Loop closure enables SLAM to work. So in real life, doing SLAM without a map is pretty hard.

• Don’t make a false Loop Closure. It’s better to miss a real one than to add a fake one, since loop closures have a lot of weight

There is error/uncertainty in the lidar and odometry measurements.

We generate a binary Occupancy Grid, where the white cells are where you are fully confidence, black are occupied, and grey are unknown.

2D SLAM Study §

TODO: insert the confluence studies?