EraseMode プロパティを置き換える方法について

Question

Naoki Ishibashi 2017-5-20

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/341116-erasemode

评论： Naoki Ishibashi 2017-5-21

現在カルマフィルターを勉強しており、他の方のコードを参考にfastslamのコードを書こうと考えております問題は、以下のコード内にあるEraseMode プロパティは2014年以降サポートされておらず、調べたところ置き換える方法があるようなのですが、元々EraseMode プロパティがどのように動作するのか書かれているものが見つけられないため、どのように書き換えればよいのかわからず困っております。以下のEraseMode部分がどのように動作し、どのように書き換えればよいか教えて頂けると幸いです。

以下コード

function data= fastslam1_sim(lm, wp)
  format compact
  path(path, '../')
  configfile;
    if SWITCH_PREDICT_NOISE==0, warning('Sampling from predict noise is necessary for FastSLAM 1.0 particle diversity'), end
    h= setup_animations(lm,wp);
    veh= [0 -WHEELBASE -WHEELBASE; 0 -1 1];
    plines=[];
    % initialisations
    particles= initialise_particles(NPARTICLES);
    xtrue= zeros(3,1);
    dt= DT_CONTROLS; % change in time between predicts
    dtsum= 0; % change in time since last observation
    ftag= 1:size(lm,2); % identifier for each landmark
    da_table= zeros(1,size(lm,2)); % data association table 
    iwp= 1; % index to first waypoint 
    G= 0; % initial steer angle
    if SWITCH_SEED_RANDOM ~= 0, rand('state',SWITCH_SEED_RANDOM), randn('state',SWITCH_SEED_RANDOM), end
    Qe= Q; Re= R;
    if SWITCH_INFLATE_NOISE==1, Qe= 2*Q; Re= 2*R; end
    if SWITCH_PROFILE, profile on -detail builtin, end
    % Main loop 
    while iwp ~= 0
        % Compute true data
        [G,iwp]= compute_steering(xtrue, wp, iwp, AT_WAYPOINT, G, RATEG, MAXG, dt);
        if iwp==0 & NUMBER_LOOPS > 1, iwp=1; NUMBER_LOOPS= NUMBER_LOOPS-1; end % path loopfs repeat
        xtrue= predict_true(xtrue, V,G, WHEELBASE,dt);
        % Add process noise
        [Vn,Gn]= add_control_noise(V,G,Q, SWITCH_CONTROL_NOISE);
        % Predict step
        for i=1:NPARTICLES
            particles(i)= predict (particles(i), Vn,Gn,Qe, WHEELBASE,dt, SWITCH_PREDICT_NOISE);
            if SWITCH_HEADING_KNOWN==1, particles(i).xf(3)= xtrue(3); end 
        end
        % Observe step
        dtsum= dtsum + dt;
        if dtsum >= DT_OBSERVE
            dtsum= 0;
            % Compute true data, then add noise
            [z,ftag_visible]= get_observations(xtrue, lm, ftag, MAX_RANGE);
            z= add_observation_noise(z,R, SWITCH_SENSOR_NOISE);
            if ~isempty(z), plines= make_laser_lines (z,xtrue); end
            % Compute (known) data associations
            Nf= size(particles(1).xf,2);
            [zf,idf,zn,da_table]= data_associate_known(z, ftag_visible, da_table, Nf);
            % Perform update
            for i=1:NPARTICLES
                if ~isempty(zf) % observe map features
                    w= compute_weight(particles(i), zf,idf, R); % w = p(z_k | x_k)
                    particles(i).w= particles(i).w * w;
                    particles(i)= feature_update(particles(i), zf, idf, R);
                end
                if ~isempty(zn) % observe new features, augment map
                    particles(i)= add_feature(particles(i), zn,R);
                end
            end
            particles= resample_particles(particles, NEFFECTIVE, SWITCH_RESAMPLE);            
        end
        % Plots
        do_plot(h, particles, xtrue, plines, veh);
    end
    if SWITCH_PROFILE, profile report, end
    data= particles;
    % 
    %
    function h= setup_animations(lm,wp)
    figure
    plot(lm(1,:),lm(2,:),'g*')
    hold on, axis equal
    plot(wp(1,:),wp(2,:), wp(1,:),wp(2,:),'ro')
    h.xt= patch(0,0,'g','erasemode','xor'); % vehicle true
    h.xm= patch(0,0,'r','erasemode','xor'); % mean vehicle estimate
    h.obs= plot(0,0,'y','erasemode','xor'); % observations
    h.xfp= plot(0,0,'r.','erasemode','background'); % estimated features (particle means)
    h.xvp= plot(0,0,'r.','erasemode','xor'); % estimated vehicle (particles)
    h.cov= plot(0,0,'erasemode','xor'); % covariances of max weight particle
    function do_plot(h, particles, xtrue, plines, veh)
    xvp = [particles.xv];
    xfp = [particles.xf];
    w = [particles.w]; 
    ii= find(w== max(w)); 
    xvmax= xvp(:,ii);
    xt= transformtoglobal(veh,xtrue);
    xm= transformtoglobal(veh,xvmax);
    set(h.xt, 'xdata', xt(1,:), 'ydata', xt(2,:))
    set(h.xm, 'xdata', xm(1,:), 'ydata', xm(2,:))
    set(h.xvp, 'xdata', xvp(1,:), 'ydata', xvp(2,:))
    if ~isempty(xfp), set(h.xfp, 'xdata', xfp(1,:), 'ydata', xfp(2,:)), end
    if ~isempty(plines), set(h.obs, 'xdata', plines(1,:), 'ydata', plines(2,:)), end
    pcov= make_covariance_ellipses(particles(ii(1)));
    if ~isempty(pcov), set(h.cov, 'xdata', pcov(1,:), 'ydata', pcov(2,:)); end
    drawnow
    function p= make_laser_lines (rb,xv)
    if isempty(rb), p=[]; return, end
    len= size(rb,2);
    lnes(1,:)= zeros(1,len)+ xv(1);
    lnes(2,:)= zeros(1,len)+ xv(2);
    lnes(3:4,:)= transformtoglobal([rb(1,:).*cos(rb(2,:)); rb(1,:).*sin(rb(2,:))], xv);
    p= line_plot_conversion (lnes);
    function p= initialise_particles(np)
    for i=1:np
        p(i).w= 1/np;
        p(i).xv= [0;0;0];
        p(i).xf= [];
        p(i).Pf= [];
    end
    function p= make_covariance_ellipses(particle)
    % part of plotting routines
    p= [];
    lenf= size(particle.xf,2);
    if lenf > 0
        N= 10;
        inc= 2*pi/N;
        phi= 0:inc:2*pi;
        circ= 2*[cos(phi); sin(phi)];
        xf= particle.xf;
        Pf= particle.Pf;
        p= zeros (2, lenf*(N+2));
        ctr= 1;
        for i=1:lenf
            ii= ctr:(ctr+N+1);
            p(:,ii)= make_ellipse(xf(:,i), Pf(:,:,i), circ);
            ctr= ctr+N+2;
        end
    end
    function p= make_ellipse(x,P,circ)
    % make a single 2-D ellipse 
    r= sqrtm_2by2(P);
    a= r*circ;
    p(2,:)= [a(2,:)+x(2) NaN];
    p(1,:)= [a(1,:)+x(1) NaN];