SLAM is more a collection of algorithms than only one of them. There are several axis in the SLAM problematic, and each problems has many solutions. Questions are:

How the robot, sensors, and maps are represented in an efficient way ?

How do we extract landmarks that can be used in landmark matching algorithms ?

What estimation algorithm do we use to keep a high probability on map and location ?

# Robot model, sensor model, map representation

# Landmark extraction

# Estimation algorithms

## Kalman filter

## Rao-Blackwellized particle filter