State Estimation
Using sensors to estimate state for autonomous vehicles
State estimation sits at the heart of autonomy. Autonomous vehicles, no matter what kind, need to know what their current state is (position, velocity, orientation, battery power level, etc.) before they can make any decisions. To do this, they need 1) sensors, that convert measurable physical quantities (speed, rotation rate, voltage, current, etc.) into digital signals 2) algorithms to combine those digital signals into a self-consistent estimate of the quantities that are relevant. Different types of autonomous vehicles have different definitions of "state" - the physical quantities that are relevant to a vehicle depend on the mission and type of vehicle. For example, a drone cares about altitude, whereas a ground vehicle does not. A rocket cares about how much fluid is left in its fuel tank, whereas a battery-powered vehicle might not. As a result, the field of state estimation is vast. There are many types of sensors, and many types of algorithms to fuse those sensors. This lens uses aerospace vehicles (drones and rockets) as the motivation to introduce the concepts.
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Introductory Sensor Fusion

  • Drone Control and the Complementary Filter

  • Understanding Sensor Fusion and Tracking, Part 1: What Is Sensor Fusion?

  • Understanding Sensor Fusion and Tracking, Part 2: Fusing a Mag, Accel, & Gyro Estimate

  • Understanding Sensor Fusion and Tracking, Part 3: Fusing a GPS and IMU to Estimate Pose

    • Advanced Filtering

  • Visually Explained: Kalman Filters

  • Kalman Filter for Beginners Explained: Recursive Filters & MATLAB | Part 1

  • Multiplicative vs. Additive Filtering for Spacecraft Attitude Determination

  • The Extended Kalman Filter (EKF): Why Taylor Expansions are Awesome

  • Identification, Estimation, and Learning

  • The Unscented Kalman Filter (UKF): A Full Tutorial. PS. Sampling Methods are Amazing

  • The Unscented Kalman Filter for Nonlinear Estimation

  • The Particle Filter: A Full Tutorial

    • Sensors

  • IMU Fundamentals, Part 1: Introduction to IMUs

  • Robotic Car - How to read Gyro Datasheets (Part 1)

  • Attitude determination of a satellite using a gyroscope and two star trackers

  • Attitude Determination: Sun Sensors, Magnetometers, and the TRIAD Method (MATLAB) | AOE 3144 L17