Objectives: Transmit the state-of-the art methods in localization and navigation algorithms and systems.

Structure: Lectures are concentrated to the first 5 weeks of the semester, where 3 labs are also given. Over the last 9 weeks studentswork on an individual project and on completing the labs. They will give a final presentation and report for the project and report for the labs.

W1: Satellite positioning (Prof. Bertrand Merminod)

  • Satellite orbit motion, Kepler’s laws, broadcast and precise ephemeris
  • Description of GPS signal structure and derivation of observables
  • Inventory of error sources, random and non-random effects
  • Derivation of mathematical models for absolute and differential positioning.
  • Estimation of the position and its precision based on least-square principle analysis
  • Overview of GNSS, new signals in space

Lab assignment: Absolute GPS positioning with and without approximation.

L2: Wireless location and state-space estimation (Dr. Cyril Botteron)

  • Fundamentals of radio-frequency propagation and positioning
  • Time and angle observables and associated error sources
  • Kalman filtering applied to kinematic positioning
  • Location with wireless computer network
  • Ultra-wide band positioning principles
  • Outdoor and indoor personal location, asset tracking

L3: Trajectory and attitude determination with inertial/satellite integration (Dr. Jan Skaloud)

  • Inertial sensors, inertial systems
    • Linear dynamical systems, stochastic differential equations
    • Inertial strapdown mechanization equations in (i,e,n) frames
    • INS strapdown error equations and calibration states
    • Alignment models
    • Redundant IMU configurations
    • Prediction, filtering, smoothing and calibration
    • No lab assignment.

    L4: Trajectory determination by optical methods and integrated sensor orientation (Dr. Jan Skaloud)

    • Colinearity condition for LiDAR, line and frame optical sensors
    • Sensor models and observations, feature matching
    • Principle of integrated sensor orientation
    • Concept of time dependent networks 
    • Dynamic networks: solution strategy and structural issues
    • Formulation of GPS/INS case in Dynamic networks
    • Numerical and practical issues

          Lab-assignment: GPS/INS/LiDAR integration via DN in 2D


    Show only week 5

    L5: Localization and navigation in mobile robotics (Prof. Alcherio Martinoli, Dr. Alexander Bahr)

    • Introduction to mobile robotics and key concepts
    • Overview of localization techniques in mobile robotics(off-board/on-board; absolute/relative)
    • Basic forward kinematic models (differential drive)
    • Odometry
    • Feature-based localization
    • Kalman filtering and particle filtering techniques applied to mobile robots
    • Multi-robot localization, cooperative localization
    • Underwater vehicle localization
         No lab assignments