Sensor Fusion

Sensors: Learn how lidar and radar work.

Kalman Filter:
Use probability distributions to fuse lidar and radar data together.
C++ Tutorial:
Review the key C++ concepts for implementing the Term 2 projects.
Kalman Filter in C++:
Build high performance filters.
Project: Pedestrian Tracking

Fuse sanitized lidar and radar data together to track a pedestrian.

Fuse real-world lidar and radar data together to track a pedestrian.

Motion: Study
how motion and probability affect your belief about where you are in the world.
Markov Localization:
Use a Bayesian filter to localize the vehicle.
Bayesian Filter:
Implement a Bayesian filter for localization.
Egomotion:
Estimate the position of the car over time given different sensor data.
Sampling for Localization:
Use a particle filter to localize the vehicle.
Particle Filter:
Implement a particle filter in C++.
Project: Kidnapped Vehicle

Implement a particle filter to take real-world data and localize a lost vehicle.

Stability:
Investigate the properties of stable and unstable systems.
Open-Loop Control:
Implement a controller in which the actuation is independent of the controller output.
Closed-Loop Control:
Implement a controller in which the actuation is dependent on the controller output.
PID Controller:
Implement a Proportional-Integral-Derivative controller.
Linear Quadratic
Regulator: Optimize the PID controller using a quadratic system of equations.
Project: Lane-Keeping

Implement a controller to overcome disturbances and keep a simulated vehicle in its path.

Vehicle control
Kinematic and dynamic vehicle models.
Model predictive control
Frame the control problem as an optimization problem over time horizons.
Project: Model predictive control

Drive the vehicle around the track even with the additional latency between commands!