QuaternionStepIntegratorType.java
/*
* Copyright (C) 2022 Alberto Irurueta Carro (alberto@irurueta.com)
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.irurueta.navigation.inertial.calibration.gyroscope;
/**
* Indicates type of quaternion integrator step.
* Different types exist with different levels of accuracy and computation complexity.
*/
public enum QuaternionStepIntegratorType {
/**
* Performs quaternion integration using Euler's method, which is the less accurate and has the
* smallest computational complexity.
*/
EULER_METHOD,
/**
* Performs quaternion integration using mid-point algorithm, which offers a medium accuracy and
* computational complexity.
*/
MID_POINT,
/**
* Performs quaternion integration using Runge-Kutta of 4th order (aka RK4) algorithm, which
* offers high accuracy at the expense of higher computational complexity.
*/
RUNGE_KUTTA,
/**
* Performs quaternion integration Based on Young Soo Suh. "Orientation estimation using a quaternion-based
* indirect Kalman filter with adaptive estimation of external acceleration". 2010.
* This method can achieve higher accuracy than Runge-Kutta
*/
SUH,
/**
* Performs quaternion integration based on Trawny, N. "Indirect Kalman Filter for 3D Attitude Estimation". 2005,
* which offers a medium accuracy and computational complexity.
*/
TRAWNY,
/**
* Performs quaternion integration based on Yuan, S. "Quaternion-based Unscented Kalman Filter for Real-time". 2015,
* which offers a medium accuracy and computational complexity.
*/
YUAN
}