comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/paskin1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% This is like robot1, except we only use a Kalman filter.
+% The goal is to study how the precision matrix changes.
+
+seed = 1;
+rand('state', seed);
+randn('state', seed);
+
+if 0
+  T = 20;
+  ctrl_signal = [repmat([1 0]', 1, T/4) repmat([0 1]', 1, T/4) ...
+		 repmat([-1 0]', 1, T/4) repmat([0 -1]', 1, T/4)];
+else
+  T = 60;
+  ctrl_signal = repmat([1 0]', 1, T);
+end
+
+nlandmarks = 6;
+if 0
+  true_landmark_pos = [1 1;
+		    4 1;
+		    4 4;
+		    1 4]';
+else
+  true_landmark_pos = 10*rand(2,nlandmarks);
+end
+if 0
+figure(1); clf
+hold on
+for i=1:nlandmarks
+  %text(true_landmark_pos(1,i), true_landmark_pos(2,i), sprintf('L%d',i));
+  plot(true_landmark_pos(1,i), true_landmark_pos(2,i), '*')
+end
+hold off
+end
+
+init_robot_pos = [0 0]';
+
+true_robot_pos = zeros(2, T);
+true_data_assoc = zeros(1, T);
+true_rel_dist = zeros(2, T);
+for t=1:T
+  if t>1
+    true_robot_pos(:,t) = true_robot_pos(:,t-1) + ctrl_signal(:,t);
+  else
+    true_robot_pos(:,t) = init_robot_pos + ctrl_signal(:,t);
+  end
+  nn = argmin(dist2(true_robot_pos(:,t)', true_landmark_pos'));
+  %true_data_assoc(t) = nn;
+  %true_data_assoc = wrap(t, nlandmarks); % observe 1, 2, 3, 4, 1, 2, ...
+  true_data_assoc  = sample_discrete(normalise(ones(1,nlandmarks)),1,T);
+  true_rel_dist(:,t) = true_landmark_pos(:, nn) - true_robot_pos(:,t);
+end
+
+R = 1e-3*eye(2); % noise added to observation
+Q = 1e-3*eye(2); % noise added to robot motion
+
+% Create data set
+obs_noise_seq = sample_gaussian([0 0]', R, T)';
+obs_rel_pos = true_rel_dist + obs_noise_seq;
+%obs_rel_pos = true_rel_dist;
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Create params for inference
+
+% X(t) = A X(t-1) + B U(t) + noise(Q) 
+
+% [L1]  = [1     ]  * [L1]       + [0]  * Ut  + [0   ]
+% [L2]    [  1   ]    [L2]         [0]          [ 0  ]
+% [R ]t   [     1]    [R ]t-1      [1]          [   Q]
+
+% Y(t)|S(t)=s  = C(s) X(t) + noise(R)
+% Yt|St=1 = [1 0 -1]  * [L1]  + R
+%                       [L2]    
+%                       [R ]    
+
+% Create indices into block structure
+bs = 2*ones(1, nlandmarks+1); % sizes of blocks in state space
+robot_block =  block(nlandmarks+1, bs);
+for i=1:nlandmarks
+  landmark_block(:,i) = block(i, bs)';
+end
+Xsz = 2*(nlandmarks+1); % 2 values for each landmark plus robot
+Ysz = 2; % observe relative location
+Usz = 2; % input is (dx, dy)
+
+
+% create block-diagonal trans matrix for each switch
+A = zeros(Xsz, Xsz);
+for i=1:nlandmarks
+  bi = landmark_block(:,i);
+  A(bi, bi) = eye(2);
+end
+bi = robot_block;
+A(bi, bi) = eye(2);
+A = repmat(A, [1 1 nlandmarks]); % same for all switch values
+
+% create block-diagonal system cov
+
+
+Qbig = zeros(Xsz, Xsz);
+bi = robot_block;
+Qbig(bi,bi) = Q; % only add noise to robot motion
+Qbig = repmat(Qbig, [1 1 nlandmarks]);
+
+% create input matrix
+B = zeros(Xsz, Usz);
+B(robot_block,:) = eye(2); % only add input to robot position
+B = repmat(B, [1 1 nlandmarks]);
+
+% create observation matrix for each value of the switch node
+% C(:,:,i) = (0 ... I ... -I) where the I is in the i'th posn.
+% This computes L(i) - R
+C = zeros(Ysz, Xsz, nlandmarks);
+for i=1:nlandmarks
+  C(:, landmark_block(:,i), i) = eye(2); 
+  C(:, robot_block, i) = -eye(2);
+end
+
+% create observation cov for each value of the switch node
+Rbig = repmat(R, [1 1 nlandmarks]);
+
+% initial conditions
+init_x = zeros(Xsz, 1);
+init_v = zeros(Xsz, Xsz);
+bi = robot_block;
+init_x(bi) = init_robot_pos;
+%init_V(bi, bi) = 1e-5*eye(2); % very sure of robot posn
+init_V(bi, bi) = Q; % simualate uncertainty due to 1 motion step
+for i=1:nlandmarks
+  bi = landmark_block(:,i);
+  init_V(bi,bi)= 1e5*eye(2); % very uncertain of landmark psosns
+  %init_x(bi) = true_landmark_pos(:,i);
+  %init_V(bi,bi)= 1e-5*eye(2); % very sure of landmark psosns
+end
+
+%k = nlandmarks-1; % exact
+k = 3;
+ndx = {};
+for t=1:T
+  landmarks = unique(true_data_assoc(t:-1:max(t-k,1)));
+  tmp = [landmark_block(:, landmarks) robot_block'];
+  ndx{t} = tmp(:);
+end
+
+[xa, Va] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ...
+				     'model', true_data_assoc, 'u', ctrl_signal, 'B', B, ...
+		       'ndx', ndx);
+
+[xe, Ve] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ...
+				     'model', true_data_assoc, 'u', ctrl_signal, 'B', B);
+
+
+if 0
+est_robot_pos = x(robot_block, :);
+est_robot_pos_cov = V(robot_block, robot_block, :);
+
+for i=1:nlandmarks
+  bi = landmark_block(:,i);
+  est_landmark_pos(:,i) = x(bi, T);
+  est_landmark_pos_cov(:,:,i) = V(bi, bi, T);
+end
+end
+
+
+
+nrows = 10;
+stepsize = T/(2*nrows);
+ts = 1:stepsize:T;
+
+if 1 % plot
+  
+clim = [0 max(max(Va(:,:,end)))];
+
+figure(2)
+if 0
+  imagesc(Ve(1:2:end,1:2:end, T))
+  clim = get(gca,'clim');
+else
+  i = 1;
+  for t=ts(:)'
+    subplot(nrows,2,i)
+    i = i + 1;
+    imagesc(Ve(1:2:end,1:2:end, t))
+    set(gca, 'clim', clim)
+    colorbar
+  end
+end
+suptitle('exact')
+
+
+figure(3)
+if 0
+  imagesc(Va(1:2:end,1:2:end, T))
+  set(gca,'clim', clim)
+else
+  i = 1;
+  for t=ts(:)'
+    subplot(nrows,2,i)
+    i = i+1;
+    imagesc(Va(1:2:end,1:2:end, t))
+    set(gca, 'clim', clim)
+    colorbar
+  end
+end
+suptitle('approx')
+
+
+figure(4)
+i = 1;
+for t=ts(:)'
+  subplot(nrows,2,i)
+  i = i+1;
+  Vd = Va(1:2:end,1:2:end, t) - Ve(1:2:end,1:2:end,t);
+  imagesc(Vd)
+  set(gca, 'clim', clim)
+  colorbar
+end
+suptitle('diff')
+
+end % all plot
+
+
+for t=1:T
+  i = 1:2*nlandmarks;
+  denom = Ve(i,i,t) + (Ve(i,i,t)==0);
+  Vd =(Va(i,i,t)-Ve(i,i,t)) ./ denom;
+  Verr(t) = max(Vd(:));
+end
+figure(6); plot(Verr)
+title('max relative Verr')
+
+for t=1:T
+  %err(t)=rms(xa(:,t), xe(:,t));
+  err(t)=rms(xa(1:end-2,t), xe(1:end-2,t)); % exclude robot
+end
+figure(5);plot(err)
+title('rms mean pos')