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⛄ 内容介绍
卡尔曼滤波器在移动机器人的导航与控制领域应用十分广泛,目前也正在计算机的其他领域有着广泛的应用.为了更好地理解卡尔曼滤波器的基本原理并将卡尔曼滤波更好地利用在更多领域,结合卡尔曼滤波器在机器人定位中的应用阐述卡尔曼滤波的系统模型以及卡尔曼滤波器不断预测与更新的过程,通过MATLAB软件对该方法进行仿真试验,最后给出观测结果.
⛄ 部分代码
% estimates the relative distance and orientation between...
% moving robot, and stationary robot that acts as a landmark
function movingRobot = estimateRelativePose(movingRobot,stationaryRobot)
%######################### MEASURED DISTANCES AND ANGLES ##################
% calculate measured relative distance
dx=stationaryRobot.groundTruth(end,1)-movingRobot.groundTruth(end,1);
dy=stationaryRobot.groundTruth(end,2)-movingRobot.groundTruth(end,2);
% rng('shuffle');
rho=sqrt((dx)^2+(dy)^2)+movingRobot.sigma_rho*randn(1);
% calculate measured relative angle
% rng('shuffle');
phi=normalizeAngle(normalizeAngle(normalizeAngle(atan2(dy,dx))-...
movingRobot.groundTruth(end,3))+...
normalizeAngle(movingRobot.sigma_phi*randn(1)));
% combine measured rho and phi
movingRobot.Z=[rho;phi];
%######################### ESTIMATED DISTANCES AND ANGLES ##################
% calculate estimated relative distance
dx_bar=stationaryRobot.mu(end,1)-movingRobot.mu_bar(1);
dy_bar=stationaryRobot.mu(end,2)-movingRobot.mu_bar(2);
rho_bar=sqrt((dx_bar)^2+(dy_bar)^2);
% calculate estimated relative angle
phi_bar=normalizeAngle(normalizeAngle(normalizeAngle(atan2(dy_bar,dx_bar))-...
movingRobot.mu_bar(3)));
% combine measured rho_bar and phi_bar
movingRobot.Z_bar=[rho_bar;phi_bar];
movingRobot.Z_diff=evaluateRelativePoseDifference(movingRobot.Z,movingRobot.Z_bar);
⛄ 运行结果
⛄ 参考文献
[1]马世强. 一种基于卡尔曼滤波器的多目标跟踪算法研究[J]. 电子世界, 2016(23):3.
