Please visit the web page of our lab: www.autonomousrobotslab.com
Dr. Kostas Alexis
  • Home
  • News
  • Research
    • Autonomous Navigation and Exploration
    • Solar-powered UAVs
    • Agile and Physical Interaction Control
    • Localization and 3D Reconstruction
    • Augmented Reality
    • Marine Robotics
    • Autonomous Robots Arena
    • Projects
  • Publications
  • Group
    • Positions
  • Courses
    • Introduction to Aerial Robotics >
      • Online Textbook >
        • Modeling >
          • Frame Rotations and Representations
          • Multirotor Dynamics
        • State Estimation >
          • Inertial Sensors
          • The Kalman Filter
        • Flight Control >
          • PID Control
          • LQR Control
          • Linear Model Predictive Control
        • Motion Planning >
          • Holonomic Vehicle BVS
          • Dubins Airplane
          • Collision-free Navigation
          • Structural Inspection Path Planning
        • Simulation Tools >
          • Simulations with SimPy
          • MATLAB & Simulink
          • RotorS Simulator >
            • RotorS Simulator Video Examples
      • Literature and Links
      • RotorS Simulator
      • Student Projects
      • Homework Assignments
      • Independent Study
      • Video Explanations
      • Syllabus
      • Grade Statistics
    • Autonomous Mobile Robot Design >
      • Semester Projects
      • Code Repository
      • Literature and Links
      • RotorS Simulator
      • Video Explanations
      • Resources for Semester Projects
      • Syllabus
      • Grade Statistics
    • Robotics Short Seminars
    • Outreach >
      • Autonomous Robots Camp >
        • RotorS Simulator
  • Student Projects
    • Undergraduate Researchers Needed
  • Contact

State Estimation

The goal of this section is to provide an overview and intuitive introduction on the basic concepts of state estimation. State estimation for aerial robotics is the field that deals with the challenge of using on-board sensors and appropriate mathematical tools in order to estimate the vehicle state (typically the combination of position, velocity, orientation and angular velocity, while in more complicated cases we also estimate higher-order states).

Within the framework of this course we are mostly interested in state estimation systems that rely on inertial sensors and GPS feeds. Once introduced to these sensing modalities, a subsection on the concepts of Kalman FIlter follows. More specifically, this section contains the following subsections:
  1. Inertial Sensors
  2. The Kalman Filter
  3. Inertial Navigation Systems
Proudly powered by Weebly