matlab面向对象设计---类的概念和使用

最新推荐文章于 2025-02-08 08:33:22 发布

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最新推荐文章于 2025-02-08 08:33:22 发布

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本文介绍了一种基于 Madgwick 的 IMU 和 AHRS 算法实现，该算法通过梯度下降法对传感器读数进行校正，并提供了两种更新方法：一种用于同时使用陀螺仪、加速度计和磁力计的情况，另一种仅适用于陀螺仪和加速度计。

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classdef MadgwickAHRS < handle
%MADGWICKAHRS Implementation of Madgwick's IMU and AHRS algorithms
%
% For more information see:
% http://www.x-io.co.uk/node/8#open_source_ahrs_and_imu_algorithms
%
% Date Author Notes
% 28/09/2011 SOH Madgwick Initial release

%% Public properties
properties (Access = public)
SamplePeriod = 1/256;
Quaternion = [1 0 0 0]; % output quaternion describing the Earth relative to the sensor
Beta = 1; % algorithm gain
end

%% Public methods
methods (Access = public)
function obj = MadgwickAHRS(varargin)
for i = 1:2:nargin
if strcmp(varargin{i}, 'SamplePeriod'), obj.SamplePeriod = varargin{i+1};
elseif strcmp(varargin{i}, 'Quaternion'), obj.Quaternion = varargin{i+1};
elseif strcmp(varargin{i}, 'Beta'), obj.Beta = varargin{i+1};
else error('Invalid argument');
end
end;
end
function obj = Update(obj, Gyroscope, Accelerometer, Magnetometer)
q = obj.Quaternion; % short name local variable for readability

% Normalise accelerometer measurement
if(norm(Accelerometer) == 0), return; end % handle NaN
Accelerometer = Accelerometer / norm(Accelerometer); % normalise magnitude

% Normalise magnetometer measurement
if(norm(Magnetometer) == 0), return; end % handle NaN
Magnetometer = Magnetometer / norm(Magnetometer); % normalise magnitude

% Reference direction of Earth's magnetic feild
h = quaternProd(q, quaternProd([0 Magnetometer], quaternConj(q)));
b = [0 norm([h(2) h(3)]) 0 h(4)];

% Gradient decent algorithm corrective step
F = [2*(q(2)*q(4) - q(1)*q(3)) - Accelerometer(1)
2*(q(1)*q(2) + q(3)*q(4)) - Accelerometer(2)
2*(0.5 - q(2)^2 - q(3)^2) - Accelerometer(3)
2*b(2)*(0.5 - q(3)^2 - q(4)^2) + 2*b(4)*(q(2)*q(4) - q(1)*q(3)) - Magnetometer(1)
2*b(2)*(q(2)*q(3) - q(1)*q(4)) + 2*b(4)*(q(1)*q(2) + q(3)*q(4)) - Magnetometer(2)
2*b(2)*(q(1)*q(3) + q(2)*q(4)) + 2*b(4)*(0.5 - q(2)^2 - q(3)^2) - Magnetometer(3)];
J = [-2*q(3), 2*q(4), -2*q(1), 2*q(2)
2*q(2), 2*q(1), 2*q(4), 2*q(3)
0, -4*q(2), -4*q(3), 0
-2*b(4)*q(3), 2*b(4)*q(4), -4*b(2)*q(3)-2*b(4)*q(1), -4*b(2)*q(4)+2*b(4)*q(2)
-2*b(2)*q(4)+2*b(4)*q(2), 2*b(2)*q(3)+2*b(4)*q(1), 2*b(2)*q(2)+2*b(4)*q(4), -2*b(2)*q(1)+2*b(4)*q(3)
2*b(2)*q(3), 2*b(2)*q(4)-4*b(4)*q(2), 2*b(2)*q(1)-4*b(4)*q(3), 2*b(2)*q(2)];
step = (J'*F);
step = step / norm(step); % normalise step magnitude

% Compute rate of change of quaternion
qDot = 0.5 * quaternProd(q, [0 Gyroscope(1) Gyroscope(2) Gyroscope(3)]) - obj.Beta * step';

% Integrate to yield quaternion
q = q + qDot * obj.SamplePeriod;
obj.Quaternion = q / norm(q); % normalise quaternion
end
function obj = UpdateIMU(obj, Gyroscope, Accelerometer)
q = obj.Quaternion; % short name local variable for readability

% Normalise accelerometer measurement
if(norm(Accelerometer) == 0), return; end % handle NaN
Accelerometer = Accelerometer / norm(Accelerometer); % normalise magnitude

% Gradient decent algorithm corrective step
F = [2*(q(2)*q(4) - q(1)*q(3)) - Accelerometer(1)
2*(q(1)*q(2) + q(3)*q(4)) - Accelerometer(2)
2*(0.5 - q(2)^2 - q(3)^2) - Accelerometer(3)];
J = [-2*q(3), 2*q(4), -2*q(1), 2*q(2)
2*q(2), 2*q(1), 2*q(4), 2*q(3)
0, -4*q(2), -4*q(3), 0 ];
step = (J'*F);
step = step / norm(step); % normalise step magnitude

% Compute rate of change of quaternion
qDot = 0.5 * quaternProd(q, [0 Gyroscope(1) Gyroscope(2) Gyroscope(3)]) - obj.Beta * step';

% Integrate to yield quaternion
q = q + qDot * obj.SamplePeriod;
obj.Quaternion = q / norm(q); % normalise quaternion
end
end
end