Strategic Objectives
• Master the mathematical foundations of spatial uncertainty.
• Implement robust state estimation using EKF and Factor Graphs.
• Understand the shift from classical filtering to modern optimization.
• Bridge the gap between raw sensor data and reliable spatial intelligence.
The Core Challenge
Robots face a fundamental paradox: they need a map to know where they are, but they need to know where they are to build the map.
01
The SLAM Paradox
02
Foundations of Probability
03
The Bayesian Framework
04
State Estimation Principles
05
The Kalman Filter
06
Extended Kalman Filters
07
Information Theory in SLAM
08
The Particle Filter Approach
09
Graph-Based SLAM
10
Factor Graphs
11
Non-Linear Least Squares
12
The Levenberg-Marquardt Algorithm
13
Covariance and Correlation
14
Data Association Challenges
15
Loop Closure Detection
16
Maximum A Posteriori Estimation
17
Robust Cost Functions
18
Sparse Matrix Methods
19
Lie Groups in Robotics
20
Semantic SLAM
21