Localization

Localization is the ability of a robot to know its position and orientation with sensors such as Global Navigation Satellite System:GNSS etc. In localization, Bayesian filters such as Kalman filters, histogram filter, and particle filter are widely used[31]. Fig.2 shows localization simulations using histogram filter and particle filter.

Contents

• Extended Kalman Filter Localization
  • Position Estimation Kalman Filter
  • Kalman Filter with Speed Scale Factor Correction
  • Reference
• GPS/IMU Fusion Localization with Bias Estimation
  • Covariance-based uncertainty
  • Assumptions
  • State and inertial prediction
  • GPS correction and outages
  • Comparison without bias estimation
  • Code
  • References
• Ensemble Kalman Filter Localization
  • Code Link
  • Input Vector
  • Observation Vector
• Unscented Kalman Filter localization
  • Code Link
  • Unscented Kalman Filter Algorithm
  • Sigma Points and Weights
  • Filter Design
  • Motion Model
  • Observation Model
  • UKF Parameters
  • Advantages of UKF over EKF
  • Reference
• Histogram filter localization
  • Code Link
  • Filtering algorithm
  • Reference
• Particle filter localization
  • Code Link
  • How to calculate covariance matrix from particles
  • Reference