Extended Kalman Filter - EKF Flashcards
Localization
A mathematical algorithm that uses a series of measurements over time to estimate the state of a system.
Extended Kalman Filter (EKF):
A variant of the Kalman filter that can handle nonlinear systems by linearizing them at each time step.
State Vector:
A vector that describes the complete state of a system, including position, velocity, orientation, etc.
Measurement Vector:
A vector that contains the sensor measurements used to estimate the state of a system.
Measurement Model:
A mathematical model that relates the state of a system to the sensor measurements.
Localization:
The process of determining the position and orientation of a robot or vehicle in its environment.
Sensor Fusion:
The process of combining data from multiple sensors to improve the accuracy of a localization estimate.
Odometry:
The use of sensors to estimate the motion of a robot or vehicle, typically by measuring wheel rotations or inertial forces.
Particle Filter:
A probabilistic algorithm for estimating the state of a system using a set of particles.
SLAM (Simultaneous Localization and Mapping):
The problem of building a map of an environment while simultaneously estimating the position and orientation of a robot or vehicle within it.
Dead Reckoning:
The process of estimating the position and orientation of a robot based on its previous known position and motion.
Process Model:
A mathematical model that describes how the state of a system evolves over time.
Covariance Matrix:
A matrix that describes the uncertainty in the state estimate.
Sensor footprint
determines the observed area by the sensor
