diff --git a/mr/ub8/localize.png b/mr/ub8/localize.png
new file mode 100644
index 0000000..31a3be5
--- /dev/null
+++ b/mr/ub8/localize.png
Binary files differ
diff --git a/mr/ub8/localize_jp/get_angle_bracket.m b/mr/ub8/localize_jp/get_angle_bracket.m
new file mode 100644
index 0000000..982c4cf
--- /dev/null
+++ b/mr/ub8/localize_jp/get_angle_bracket.m
@@ -0,0 +1,19 @@
+function [ p ] = get_angle_bracket( mean, cov )
+%GET_ANGLE_BRACKET Compute points visualizing 95% orientation uncertainty
+
+scale = 2; % in multiples of sigma
+length = 0.5; % length of both arms of the "bracket"
+
+p = zeros(2,3);
+
+theta = mean(3);
+sigmaTheta = sqrt(cov(3,3)); % marginalized standard deviation of theta
+th1 = theta - scale*sigmaTheta;
+th3 = theta + scale*sigmaTheta;
+
+p(:,2) = mean(1:2);
+p(:,1) = mean(1:2) + length*[cos(th1); sin(th1)];
+p(:,3) = mean(1:2) + length*[cos(th3); sin(th3)];
+
+end
+
diff --git a/mr/ub8/localize_jp/get_ellipse.m b/mr/ub8/localize_jp/get_ellipse.m
new file mode 100644
index 0000000..4f1cfb0
--- /dev/null
+++ b/mr/ub8/localize_jp/get_ellipse.m
@@ -0,0 +1,25 @@
+function [ p ] = get_ellipse( mean, cov )
+%GET_ELLIPSE Compute points on the 95% uncertainty ellipse.
+
+% Compute eigenvalues and eigenvectors of marginalized position covariance 
+[V D] = eig(cov(1:2,1:2));
+[D order] = sort(diag(D),'descend'); %sort eigenvalues in descending order
+V = V(:,order);
+evec_max = V(:, 1); % eigenvector with alrgest eigenvalue
+
+% Construct uncertainty ellipse in parallel to eigenvectors
+scale = 2.; % in multiples of sigma
+a = scale*sqrt(D(1));
+b = scale*sqrt(D(2));
+theta_grid = linspace(0,2*pi);
+ellipse_x_r  = a*cos( theta_grid );
+ellipse_y_r  = b*sin( theta_grid );
+
+% Transform to world frame
+phi = atan2(evec_max(2), evec_max(1)); % Angle between x and largest EV
+R = [cos(phi) -sin(phi); sin(phi) cos(phi)];
+p = R*[ellipse_x_r;ellipse_y_r];
+p = p + repmat(mean(1:2),1,size(p,2));
+
+end
+
diff --git a/mr/ub8/localize_jp/localize.m b/mr/ub8/localize_jp/localize.m
new file mode 100644
index 0000000..1293290
--- /dev/null
+++ b/mr/ub8/localize_jp/localize.m
@@ -0,0 +1,79 @@
+close all;
+
+% ## General parameters
+% Fix random seed  so everyone gets the same results.
+rng(230)
+% Sampling time:
+dt = 0.2;
+% System noise:
+R = diag([0.02^2, 0.02^2, 0.02^2]);
+% Measurement noise (1D measurements):
+Q = 0.5^2;
+% Construct vector with control input:
+uhc = repmat([0.1; 0.105], 1, 63);
+ust = repmat([0.1; 0.1], 1, 30);
+u = [uhc, ust, uhc, ust];
+
+% ## Compute the true trajectory:
+%    Evaluate kinematic model according to control input
+%    and add some sampled system noise.
+currx = [0; 0; 0];
+x_true = currx;
+z_meas = [];
+for i = 1:size(u,2)
+    % Evaluate kinematic Model:
+    currx = motion_diff(currx, u(:,i));
+    % Add system noise:
+    currx = currx + mvnrnd([0; 0; 0], R)';
+    % Simulate a measurement:
+    z_meas = [z_meas, measurement(currx) + mvnrnd(zeros(size(Q,1),1),Q)'];
+    % Store result for plotting
+    x_true = [x_true, currx];
+end
+
+% ## Evaluate EKF without measurements: 
+%    Estimates are based on control input alone.
+curr_mean = [0; 0; 0];
+curr_cov = diag([0, 0, 0]);
+
+nomeas_mean = curr_mean;
+nomeas_cov = reshape(curr_cov, 9, 1);
+for i = 1:size(u,2)
+    % Update mean and Jacobian G:
+    [curr_mean G] = motion_diff(curr_mean, u(:,i));
+    
+    % Update covariance matrix
+    curr_cov = G*curr_cov*(G') + R;
+    
+    % Store result for plotting
+    nomeas_mean = [nomeas_mean, curr_mean];
+    nomeas_cov = [nomeas_cov, reshape(curr_cov, 9, 1)];
+end % for each u
+plot_trajectory_cov(x_true, nomeas_mean, nomeas_cov, dt);
+
+% ## Full EKF: Estimates based on control input and measurements.
+curr_mean = [0; 0; 0];
+curr_cov = diag([0. 0, 0]);
+
+ekf_mean = curr_mean;
+ekf_cov = reshape(curr_cov, 9, 1);
+for i = 1:size(u,2)
+    % Update step:
+    [curr_mean G] = motion_diff(curr_mean, u(:,i));
+    curr_cov = G*curr_cov*(G') + R;
+    
+    % Correction step:
+    % Expected measurement:
+    [z_exp, H] = measurement(curr_mean);
+   
+    % Kalman gain:
+    K = curr_cov * (H') * inv(H*curr_cov*(H') + Q);
+    curr_mean = curr_mean + K*(z_meas(:,i) - z_exp);
+    curr_cov = (eye(3) - K*H)*curr_cov;
+    
+    % Store result for plotting
+    ekf_mean = [ekf_mean, curr_mean];
+    ekf_cov = [ekf_cov, reshape(curr_cov, 9, 1)];
+end % for each u
+
+plot_trajectory_cov(x_true, ekf_mean, ekf_cov, dt);
\ No newline at end of file
diff --git a/mr/ub8/localize_jp/measurement.m b/mr/ub8/localize_jp/measurement.m
new file mode 100644
index 0000000..35c902f
--- /dev/null
+++ b/mr/ub8/localize_jp/measurement.m
@@ -0,0 +1,16 @@
+function [ z H ] = measurement( x )
+%MEASUREMENT Measurement Model: Measure the robot's distance from origin.
+
+% YOUR CODE STARTS HERE
+
+z = sqrt(x(1)*x(1)+x(2)*x(2)); % fill this with the measurement result
+syms x1 x2 x3
+H=jacobian(sqrt(x1*x1+x2*x2),[x1;x2;x3]);
+x1=x(1);
+x2=x(2);
+H = eval(H); % fill this with the measurement Jacobian
+
+% YOUR CODE ENDS HERE
+
+end
+
diff --git a/mr/ub8/localize_jp/motion_diff.m b/mr/ub8/localize_jp/motion_diff.m
new file mode 100644
index 0000000..848f892
--- /dev/null
+++ b/mr/ub8/localize_jp/motion_diff.m
@@ -0,0 +1,27 @@
+function [ xnew G] = motion_diff( x, u )
+%MOTION_DIFF Compute differential drive motion model
+
+l = 0.1; % distance between both wheels
+
+theta = x(3);
+sl = u(1);
+sr = u(2);
+
+% YOUR CODE STARTS HERE %
+syms x1 x2 x3
+
+if (sl == sr)
+    xnew=x+sl*[cos(theta);sin(theta);0];
+    G=jacobian([x1;x2;x3]+sl*[cos(x3);sin(x3);0],[x1;x2;x3]); 
+else
+    r=(sl+sr)/(sr-sl)*l/2;
+    dtheta=(sr-sl)/l;
+    xnew=x+[r*(sin(theta+dtheta)-sin(theta));r*(-cos(theta+dtheta)+cos(theta));dtheta];
+    G=jacobian([x1;x2;x3]+[r*(sin(x3+dtheta)-sin(x3));r*(-cos(x3+dtheta)+cos(x3));dtheta],[x1;x2;x3]);
+end
+x3=theta;
+G=eval(G);
+% YOUR CODE ENDS HERE %     
+
+end
+
diff --git a/mr/ub8/localize_jp/plot_trajectory_cov.m b/mr/ub8/localize_jp/plot_trajectory_cov.m
new file mode 100644
index 0000000..5e11454
--- /dev/null
+++ b/mr/ub8/localize_jp/plot_trajectory_cov.m
@@ -0,0 +1,46 @@
+function [  ] = plot_trajectory_cov(xtrue, mean, cov, dt )
+%PLOT_TRAJECTORY_COV Plot the 2d trajectory of a robot with uncertainty
+%                    estimates
+
+% Create a new figure for this plot
+figure
+
+% Plot ground truth trajectory:
+plot(xtrue(1,:), xtrue(2,:), 'r');
+
+hold on % keep drawing into the same figure
+
+% Draw the estimate of the same trajectory.
+plot(mean(1,:), mean(2,:))
+
+% Only at some times: Visualize the estimate uncertainty.
+ellipsetime = 4/dt; % Once in 4 seconds
+em = mean(:,1:ellipsetime:end);
+ecov = cov(:,1:ellipsetime:end);
+
+% Draw uncertainty ellipses
+for i = 1:size(ecov,2)
+    s = reshape(ecov(:,i),3,3);
+    
+    % Compute and plot uncertainty ellipse for position covariance.
+    p_pos = get_ellipse(em(:,i), s);
+    plot(p_pos(1,:), p_pos(2,:),'k');
+    
+    % Compute and plot angle interval showing orientation uncertainty.
+    p_th  = get_angle_bracket(em(:,i), s);
+    plot(p_th(1,:), p_th(2,:),'k');
+end
+
+% Description & legend
+axis equal
+xlabel('x in m')
+ylabel('y in m')
+title('robot trajectory with uncertainty estimates')
+legend('true trajectory', ...
+       'estimate (mean)', ...
+       'estimate (covariance)', ...
+       'location', 'SouthEast');
+
+
+end
+