diff --git a/mr/ub8/assignment08.pdf b/mr/ub8/assignment08.pdf
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index 0000000..28f47ec
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+++ b/mr/ub8/assignment08.pdf
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diff --git a/mr/ub8/localize_ekf/get_angle_bracket.m b/mr/ub8/localize_ekf/get_angle_bracket.m
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+++ b/mr/ub8/localize_ekf/get_angle_bracket.m
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+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_ekf/get_ellipse.m b/mr/ub8/localize_ekf/get_ellipse.m
new file mode 100644
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+++ b/mr/ub8/localize_ekf/get_ellipse.m
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+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_ekf/localize.m b/mr/ub8/localize_ekf/localize.m
new file mode 100644
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+++ b/mr/ub8/localize_ekf/localize.m
@@ -0,0 +1,80 @@
+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;
+Q=1*10^(-1000)
+% 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_ekf/measurement.m b/mr/ub8/localize_ekf/measurement.m
new file mode 100644
index 0000000..fddf5e8
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+++ b/mr/ub8/localize_ekf/measurement.m
@@ -0,0 +1,15 @@
+function [ z H ] = measurement( x )
+%MEASUREMENT Measurement Model: Measure the robot's distance from origin.
+
+% YOUR CODE STARTS HERE
+
+z = sqrt(x(1)^2+x(2)^2); % fill this with the measurement result
+%H = zeros(1,3); % fill this with the measurement Jacobian
+H=[x(1)/z;
+  x(2)/z;  
+    0];
+
+% YOUR CODE ENDS HERE
+
+end
+
diff --git a/mr/ub8/localize_ekf/motion_diff.m b/mr/ub8/localize_ekf/motion_diff.m
new file mode 100644
index 0000000..1faaaa7
--- /dev/null
+++ b/mr/ub8/localize_ekf/motion_diff.m
@@ -0,0 +1,33 @@
+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 %
+deltaTheta= (sr-sl)/l;
+r= (sl+sr)/(sr-sl) *l/2;
+% fill this with the result of applying the motion model
+if(sr==sl)
+xnew= x+ [r*(sin(theta+deltaTheta)-sin(theta));
+          r*(-cos(theta+deltaTheta)-cos(theta));
+          deltaTheta];
+else
+xnew= x+[sl*cos(theta);
+        sl*sin(theta);
+        0];
+end    
+% fill this with the motion Jacobian G
+% G=zeros(3,3)
+G = [1,0,r*(cos(theta+deltaTheta)-cos(theta));
+    0,1,r*(sin(theta+deltaTheta)-sin(theta));
+    0,0,1];
+
+
+% YOUR CODE ENDS HERE %     
+
+end
+
diff --git a/mr/ub8/localize_ekf/plot_trajectory_cov.m b/mr/ub8/localize_ekf/plot_trajectory_cov.m
new file mode 100644
index 0000000..5e11454
--- /dev/null
+++ b/mr/ub8/localize_ekf/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
+
diff --git a/mr/ub8/mr8.pdf b/mr/ub8/mr8.pdf
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+++ b/mr/ub8/mr8.pdf
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diff --git a/mr/ub8/mr8.tex b/mr/ub8/mr8.tex
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+++ b/mr/ub8/mr8.tex
@@ -0,0 +1,109 @@
+\documentclass[a4paper,12pt]{scrartcl}
+\usepackage[ngerman]{babel}
+\usepackage{graphicx} %BIlder einbinden
+\usepackage{amsmath} %erweiterte Mathe-Zeichen
+\usepackage{amsfonts} %weitere fonts
+\usepackage[utf8]{inputenc} %Umlaute & Co
+\usepackage{hyperref} %Links
+\usepackage{ifthen} %ifthenelse
+\usepackage{enumerate}
+\usepackage{pdfpages}
+\usepackage{algpseudocode} %Pseudocode
+\usepackage{dsfont} % schöne Zahlenräumezeichen
+\usepackage{amssymb, amsthm} %noch stärker erweiterte Mathe-Zeichen
+\usepackage{tikz} %TikZ ist kein Zeichenprogramm
+\usetikzlibrary{trees,automata,arrows,shapes}
+\usepackage{qtree}
+
+\pagestyle{empty}
+
+
+\topmargin-50pt
+
+\newcounter{aufgabe}
+\def\tand{&}
+
+\newcommand{\makeTableLine}[2][0]{%
+  \setcounter{aufgabe}{1}%
+  \whiledo{\value{aufgabe} < #1}%
+  {%
+    #2\tand\stepcounter{aufgabe}%
+  }
+}
+
+\newcommand{\aufgTable}[1]{
+  \def\spalten{\numexpr #1 + 1 \relax}
+  \begin{tabular}{|*{\spalten}{p{1cm}|}}
+    \makeTableLine[\spalten]{A\theaufgabe}$\Sigma$~~\\ \hline
+    \rule{0pt}{15pt}\makeTableLine[\spalten]{}\\
+  \end{tabular}
+}
+
+\def\header#1#2#3#4#5#6#7{\pagestyle{empty}
+\begin{minipage}[t]{0.47\textwidth}
+\begin{flushleft}
+{\bf #4}\\
+#5
+\end{flushleft}
+\end{minipage}
+\begin{minipage}[t]{0.5\textwidth}
+\begin{flushright}
+#6 \vspace{0.5cm}\\
+%                 Number of Columns    Definition of Columns      second empty line
+% \begin{tabular}{|*{5}{C{1cm}|}}\hline A1&A2&A3&A4&$\Sigma$\\\hline&&&&\\\hline\end{tabular}\\\vspace*{0.1cm}
+\aufgTable{#7}
+\end{flushright}
+\end{minipage}
+\vspace{1cm}
+\begin{center}
+{\Large\bf Assignment #1}
+
+{(Hand-in date #3)}
+\end{center}
+}
+
+
+
+%counts the exercisenumber
+\newcounter{n}
+
+%Kommando für Aufgaben
+%\Aufgabe{AufgTitel}{Punktezahl}
+\newcommand{\Aufgabe}[2]{\stepcounter{n}
+\textbf{Exercise \arabic{n}: #1} (#2 Punkte)\\}
+
+
+
+
+\begin{document}
+    %\header{BlattNr}{Tutor}{Abgabedatum}{Vorlesungsname}{Namen}{Semester}{Anzahl Aufgaben}
+    \header{7}{}{2015-06-16}{Mobile Robots}{
+        \textit{Jan-Peter Hohloch}\\ \textit{Maximus Mutschler}
+    }{SS 15}{3}
+    \vspace{1cm}
+
+    \Aufgabe{Excercise 1}{10}
+    TODO Abbildungen\\
+    d) Measurement uncertainty $Q\rightarrow0
+    \\ K_t = \overline{\Sigma}_tC^TC^{-1}\overline{\Sigma}_t^{-1}(C^T)^{-1}\\
+    =C^{-1} vllt \rightarrow\\
+    \Sigma_t=(I-I)*\overline{\Sigma}_t=0\\
+$ 
+\Aufgabe{Excercise2}{8}    
+\begin{enumerate}[(a)]
+	\item $p(x_t|z_{t,\dots1},u_{t,\dots1})\sim N(x_t,\mu_{x,t},\sigma_{x,t})\\
+	\mu_{x,t}= a\mu_{x,t-1}+bu_t+\epsilon_t\\
+	\sigma_{x,t}= |a|\sigma_{x,t-1}\\
+	\\
+	p(x_{t}|z_{t,\dots1},u_{t,\dots1})\sim N(x_{t-1},\mu_{x,t-1},\sigma_{x,t-1})\\
+	\mu_{x,t-1}= a\mu_{x,t-2}+bu_{t-1}+\epsilon_{t-1}\\
+	\sigma_{x,t-1}= |a|\sigma_{x,t-2}\\
+	\\
+	p(z_t|x-t)\sim N(z_t,\mu_z,\sigma_z)\\
+	\mu_z=c*\mu_{x,t-1}+\delta_t \\
+	\sigma_z=|c|\sigma_{x,t-1}
+	$
+\end{enumerate}
+    
+\end{document}
+