Defined in File ukf.h
Ukf: public maplab::FilterBase¶
Unscented Kalman filter class.
Implementation of an unscenter Kalman filter (UKF). This class derives from FilterBase and overrides the predict() and correct() methods in keeping with the discrete time UKF algorithm. The algorithm was derived from the UKF Wikipedia article at (http://en.wikipedia.org/wiki/Kalman_filter#Unscented_Kalman_filter) …and this paper: J. J. LaViola, Jr., “A comparison of unscented and extended Kalman filtering for estimating quaternion motion,” in Proc. American Control Conf., Denver, CO, June 4–6, 2003, pp. 2435–2440 Obtained here: http://www.cs.ucf.edu/~jjl/pubs/laviola_acc2003.pdf
Ukf(const double alpha, const double kappa, const double beta)¶
Constructor for the Ukf class.
[in] args: - Generic argument container. It is assumed that args constains the alpha parameter, args contains the kappa parameter, and args contains the beta parameter.
correct(const LocalizationFilterMeasurement &measurement, const std::vector<size_t> &update_vector)¶
Carries out the correct step in the predict/update cycle.
[in] measurement: - The measurement to fuse with our estimate
predict(const LocalizationFilterPrediction &odom_prediction = LocalizationFilterPrediction())¶
Carries out the predict step in the predict/update cycle.
Projects the state and error matrices forward using a model of the vehicle’s motion.
[in] referenceTime: - The time at which the prediction is being made
[in] odom_prediction: - The predicted state from the odometry
The UKF sigma points.
Used to sample possible next states during prediction.
This matrix is used to generate the sigmaPoints_.
The weights associated with each sigma point when generating a new state.
The weights associated with each sigma point when calculating a predicted estimateErrorCovariance_.
Used in weight generation for the sigma points.