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| void | correct (const Measurement &measurement) |
| | Carries out the correct step in the predict/update cycle. More...
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| void | predict (const double referenceTime, const double delta) |
| | Carries out the predict step in the predict/update cycle. More...
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| | Ukf (std::vector< double > args) |
| | Constructor for the Ukf class. More...
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| | ~Ukf () |
| | Destructor for the Ukf class. More...
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| void | computeDynamicProcessNoiseCovariance (const Eigen::VectorXd &state, const double delta) |
| | Computes a dynamic process noise covariance matrix using the parameterized state. More...
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| | FilterBase () |
| | Constructor for the FilterBase class. More...
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| const Eigen::VectorXd & | getControl () |
| | Returns the control vector currently being used. More...
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| double | getControlTime () |
| | Returns the time at which the control term was issued. More...
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| bool | getDebug () |
| | Gets the value of the debug_ variable. More...
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| const Eigen::MatrixXd & | getEstimateErrorCovariance () |
| | Gets the estimate error covariance. More...
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| bool | getInitializedStatus () |
| | Gets the filter's initialized status. More...
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| double | getLastMeasurementTime () |
| | Gets the most recent measurement time. More...
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| const Eigen::VectorXd & | getPredictedState () |
| | Gets the filter's predicted state, i.e., the state estimate before correct() is called. More...
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| const Eigen::MatrixXd & | getProcessNoiseCovariance () |
| | Gets the filter's process noise covariance. More...
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| double | getSensorTimeout () |
| | Gets the sensor timeout value (in seconds) More...
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| const Eigen::VectorXd & | getState () |
| | Gets the filter state. More...
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| virtual void | processMeasurement (const Measurement &measurement) |
| | Does some final preprocessing, carries out the predict/update cycle. More...
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| void | reset () |
| | Resets filter to its unintialized state. More...
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| void | setControl (const Eigen::VectorXd &control, const double controlTime) |
| | Sets the most recent control term. More...
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| void | setControlParams (const std::vector< int > &updateVector, const double controlTimeout, const std::vector< double > &accelerationLimits, const std::vector< double > &accelerationGains, const std::vector< double > &decelerationLimits, const std::vector< double > &decelerationGains) |
| | Sets the control update vector and acceleration limits. More...
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| void | setDebug (const bool debug, std::ostream *outStream=NULL) |
| | Sets the filter into debug mode. More...
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| void | setEstimateErrorCovariance (const Eigen::MatrixXd &estimateErrorCovariance) |
| | Manually sets the filter's estimate error covariance. More...
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| void | setLastMeasurementTime (const double lastMeasurementTime) |
| | Sets the filter's last measurement time. More...
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| void | setProcessNoiseCovariance (const Eigen::MatrixXd &processNoiseCovariance) |
| | Sets the process noise covariance for the filter. More...
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| void | setSensorTimeout (const double sensorTimeout) |
| | Sets the sensor timeout. More...
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| void | setState (const Eigen::VectorXd &state) |
| | Manually sets the filter's state. More...
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| void | setUseDynamicProcessNoiseCovariance (const bool dynamicProcessNoiseCovariance) |
| | Enables dynamic process noise covariance calculation. More...
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| void | validateDelta (double &delta) |
| | Ensures a given time delta is valid (helps with bag file playback issues) More...
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| virtual | ~FilterBase () |
| | Destructor for the FilterBase class. More...
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| std::vector< double > | covarWeights_ |
| | The weights associated with each sigma point when calculating a predicted estimateErrorCovariance_. More...
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| double | lambda_ |
| | Used in weight generation for the sigma points. More...
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| std::vector< Eigen::VectorXd > | sigmaPoints_ |
| | The UKF sigma points. More...
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| std::vector< double > | stateWeights_ |
| | The weights associated with each sigma point when generating a new state. More...
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| bool | uncorrected_ |
| | Used to determine if we need to re-compute the sigma points when carrying out multiple corrections. More...
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| Eigen::MatrixXd | weightedCovarSqrt_ |
| | This matrix is used to generate the sigmaPoints_. More...
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| std::vector< double > | accelerationGains_ |
| | Gains applied to acceleration derived from control term. More...
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| std::vector< double > | accelerationLimits_ |
| | Caps the acceleration we apply from control input. More...
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| Eigen::VectorXd | controlAcceleration_ |
| | Variable that gets updated every time we process a measurement and we have a valid control. More...
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| double | controlTimeout_ |
| | Timeout value, in seconds, after which a control is considered stale. More...
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| std::vector< int > | controlUpdateVector_ |
| | Which control variables are being used (e.g., not every vehicle is controllable in Y or Z) More...
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| Eigen::MatrixXd | covarianceEpsilon_ |
| | Covariance matrices can be incredibly unstable. We can add a small value to it at each iteration to help maintain its positive-definite property. More...
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| std::ostream * | debugStream_ |
| | Used for outputting debug messages. More...
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| std::vector< double > | decelerationGains_ |
| | Gains applied to deceleration derived from control term. More...
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| std::vector< double > | decelerationLimits_ |
| | Caps the deceleration we apply from control input. More...
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| Eigen::MatrixXd | dynamicProcessNoiseCovariance_ |
| | Gets updated when useDynamicProcessNoise_ is true. More...
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| Eigen::MatrixXd | estimateErrorCovariance_ |
| | This matrix stores the total error in our position estimate (the state_ variable). More...
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| Eigen::MatrixXd | identity_ |
| | We need the identity for a few operations. Better to store it. More...
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| bool | initialized_ |
| | Whether or not we've received any measurements. More...
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| double | lastMeasurementTime_ |
| | Tracks the time the filter was last updated using a measurement. More...
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| Eigen::VectorXd | latestControl_ |
| | Latest control term. More...
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| double | latestControlTime_ |
| | The time of reception of the most recent control term. More...
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| Eigen::VectorXd | predictedState_ |
| | Holds the last predicted state of the filter. More...
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| Eigen::MatrixXd | processNoiseCovariance_ |
| | As we move through the world, we follow a predict/update cycle. If one were to imagine a scenario where all we did was make predictions without correcting, the error in our position estimate would grow without bound. This error is stored in the stateEstimateCovariance_ matrix. However, this matrix doesn't answer the question of how much our error should grow for each time step. That's where the processNoiseCovariance matrix comes in. When we make a prediction using the transfer function, we add this matrix (times deltaT) to the state estimate covariance matrix. More...
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| double | sensorTimeout_ |
| | The updates to the filter - both predict and correct - are driven by measurements. If we get a gap in measurements for some reason, we want the filter to continue estimating. When this gap occurs, as specified by this timeout, we will continue to call predict() at the filter's frequency. More...
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| Eigen::VectorXd | state_ |
| | This is the robot's state vector, which is what we are trying to filter. The values in this vector are what get reported by the node. More...
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| Eigen::MatrixXd | transferFunction_ |
| | The Kalman filter transfer function. More...
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| Eigen::MatrixXd | transferFunctionJacobian_ |
| | The Kalman filter transfer function Jacobian. More...
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| bool | useControl_ |
| | Whether or not we apply the control term. More...
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| bool | useDynamicProcessNoiseCovariance_ |
| | If true, uses the robot's vehicle state and the static process noise covariance matrix to generate a dynamic process noise covariance matrix. More...
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| virtual bool | checkMahalanobisThreshold (const Eigen::VectorXd &innovation, const Eigen::MatrixXd &invCovariance, const double nsigmas) |
| | Tests if innovation is within N-sigmas of covariance. Returns true if passed the test. More...
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| double | computeControlAcceleration (const double state, const double control, const double accelerationLimit, const double accelerationGain, const double decelerationLimit, const double decelerationGain) |
| | Method for settings bounds on acceleration values derived from controls. More...
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| void | prepareControl (const double referenceTime, const double predictionDelta) |
| | Converts the control term to an acceleration to be applied in the prediction step. More...
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| virtual void | wrapStateAngles () |
| | Keeps the state Euler angles in the range [-pi, pi]. More...
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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
Definition at line 60 of file ukf.h.
Public Member Functions inherited from RobotLocalization::FilterBase