diff --git a/mr/ub9/loc_framework/ukf_correction.m b/mr/ub9/loc_framework/ukf_correction.m
index aaa2a1f..57cff12 100644
--- a/mr/ub9/loc_framework/ukf_correction.m
+++ b/mr/ub9/loc_framework/ukf_correction.m
@@ -9,20 +9,19 @@
 for i =1:size(X,2)
     Z(:,i)= measurement(X(:,i));
 end
-expX=pred_mean;
-covX=pred_cov;
-%[expX,covX]=ukf_estimate_normal( X, w );
+%Z=rowfun(measurement,X); %doesn't work like this...
+
 [expZ,CovOfIn]=ukf_estimate_normal( Z, w );
 CovOfIn = CovOfIn+Q;
 
 CrossCov = zeros(size(X,1),size(z,1));
 for i =1:size(X,2)
-    CrossCov = CrossCov+ w(i)*(X(:,i)-expX)*(Z(:,i)-expZ)';
+    CrossCov = CrossCov+ w(i)*(X(:,i)-pred_mean)*(Z(:,i)-expZ)';
 end
 
 K= CrossCov*inv(CovOfIn);
-mean= expX+K*(z-expZ);
-cov= covX * K*CovOfIn*K';
+mean= pred_mean+K*(z-expZ);
+cov= pred_cov - K*CovOfIn*K';
 
 
 
diff --git a/mr/ub9/loc_framework/ukf_sigma_points.m b/mr/ub9/loc_framework/ukf_sigma_points.m
index afda9c4..a185737 100644
--- a/mr/ub9/loc_framework/ukf_sigma_points.m
+++ b/mr/ub9/loc_framework/ukf_sigma_points.m
@@ -24,11 +24,6 @@
 w(1)=1/3;
 w(2:n)=(1-w0)/(2*nx);
 
-if (sqrtm(cov)^2 ~= cov)
-    cov
-    sqrtm(cov)^2
-end
-
 RootN= sqrt(nx/(1-w0))*sqrtm(cov);
 
 for i =1:nx
diff --git a/mr/ub9/loc_jp/get_angle_bracket.m b/mr/ub9/loc_jp/get_angle_bracket.m
deleted file mode 100644
index 89daaf0..0000000
--- a/mr/ub9/loc_jp/get_angle_bracket.m
+++ /dev/null
@@ -1,19 +0,0 @@
-function [ p ] = get_angle_bracket( mean, cov )
-%GET_ANGLE_BRACKET Compute points visualizing 95% orientation uncertainty
-
-scale = 3; % 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/ub9/loc_jp/get_ellipse.m b/mr/ub9/loc_jp/get_ellipse.m
deleted file mode 100644
index 83342f4..0000000
--- a/mr/ub9/loc_jp/get_ellipse.m
+++ /dev/null
@@ -1,28 +0,0 @@
-function [ p ] = get_ellipse( mean, cov, scale )
-%GET_ELLIPSE Compute points on uncertainty ellipse
-
-if nargin < 3
-    scale = 3.0;
-end
-
-% 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
-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/ub9/loc_jp/localize_ekf.m b/mr/ub9/loc_jp/localize_ekf.m
deleted file mode 100644
index 03bdad9..0000000
--- a/mr/ub9/loc_jp/localize_ekf.m
+++ /dev/null
@@ -1,57 +0,0 @@
-% ### Run some initialization initialization steps common to all filters
-localize_init;
-
-% ## 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)
-    % Prediction 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);
-
-disp('final result WITH measurements')
-final_mean = curr_mean
-final_cov = curr_cov
-final_err = final_mean - x_true(:,end);
-err_pos = norm(final_err(1:2))
-err_yaw = abs(final_err(3))
\ No newline at end of file
diff --git a/mr/ub9/loc_jp/localize_init.m b/mr/ub9/loc_jp/localize_init.m
deleted file mode 100644
index 9686363..0000000
--- a/mr/ub9/loc_jp/localize_init.m
+++ /dev/null
@@ -1,36 +0,0 @@
-close all
-
-% ## General parameters
-% Fix random seed so everyone gets the same true trajectory.
-rng(51)
-% Sampling time:
-dt = 0.2;
-% System noise:
-R = diag([0.02^2, 0.02^2, 0.02^2]);
-% Measurement noise (1D measurements):
-Q = eye(4)*1.0^2;
-% Construct vector with control input:
-uhc = repmat([0.1; 0.105], 1, 100);
-uhc2 = repmat([0.105; 0.1], 1, 100);
-ust = repmat([0.1; 0.1], 1, 30);
-u = [uhc, ust, uhc2, ust];
-
-% ## Compute the true trajectory and measurements:
-%    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
-
-% Reinitialize random seed generator for random sampling
-rng('shuffle')
\ No newline at end of file
diff --git a/mr/ub9/loc_jp/localize_pf.m b/mr/ub9/loc_jp/localize_pf.m
deleted file mode 100644
index 47c92fa..0000000
--- a/mr/ub9/loc_jp/localize_pf.m
+++ /dev/null
@@ -1,71 +0,0 @@
-% ### Run some initialization initialization steps common to all filters
-localize_init;
-
-% ### Important parameters:
-nSamples = 100;
-ESSmin = 0.7*nSamples;
-
-% ### Evaluate PF without measurements: 
-%    Estimates are based on control input alone.
-
-currx = zeros(3,1,nSamples);
-w = ones(nSamples,1)/nSamples;
-pf_x = currx;
-pf_w = permute(w, [2 3 1]);
-
-% Pay attention to the representation of particles in pf_x: 
-% A trajectory is stored as a 2D matrix [x_0, x_1, x_2, x_3]
-% Columns: different time steps
-% Trajectories of all particles are concatenated along a third dimension,
-% the result is a 3D tensor.
-% The pose (3x1 vector) of particle m at timestep i can be accessed using:
-%    pf_x(:,i,m);
-
-for i = 1:size(u,2)
-    currx = pf_prediction(currx, u(:,i), R, @motion_diff);
-  
-    pf_x = cat(2,pf_x, currx);
-    currw = permute(w, [2 3 1]);
-    pf_w = cat(2,pf_w, currw);
-end % for each control input
-
-plot_trajectory_particles(x_true, pf_x, pf_w, dt);
-
-
-% ### Evaluate full PF with measurements:
-currx = zeros(3,1,nSamples);
-w = ones(nSamples,1)/nSamples;
-pf_x = currx;
-pf_w = permute(w, [2 3 1]);
-
-for i = 1:size(u,2)
-    % Prediction step:
-    currx = pf_prediction(currx, u(:,i), R, @motion_diff);
-
-    % Reweighting step:
-    w = pf_reweight(currx, w, z_meas(:,i), Q, @measurement);
- 
-    % Save current estimates to trajectory. Important:
-    % It is best to do this after reweighting (to incorporate the latest
-    % measurement) but before resampling (because of the sample
-    % impoverishment problem).
-    pf_x = cat(2,pf_x, currx);
-    currw = permute(w, [2 3 1]);
-    pf_w = cat(2,pf_w, currw);
-    
-    % Resampling step:
-    currx = pf_resample(currx,w);
-    % Do not forget to reset weights after resampling:
-    w = ones(nSamples,1)/nSamples;
-    
-end % for each control input
-
-plot_trajectory_particles(x_true, pf_x, pf_w, dt);
-
-tmp = permute(currx, [3, 1, 2]).*repmat(w,1,3,1)*nSamples;
-disp('final result WITH measurements')
-final_cov = cov(tmp)
-final_mean = mean(tmp)'
-final_err = final_mean - x_true(:,end);
-err_pos = norm(final_err(1:2))
-err_yaw = abs(final_err(3))
\ No newline at end of file
diff --git a/mr/ub9/loc_jp/localize_ukf.m b/mr/ub9/loc_jp/localize_ukf.m
deleted file mode 100644
index 557b336..0000000
--- a/mr/ub9/loc_jp/localize_ukf.m
+++ /dev/null
@@ -1,42 +0,0 @@
-% ### Run some initialization initialization steps common to all filters
-localize_init;
-
-% ## Evaluate UKF 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)
-    [curr_mean, curr_cov] = ukf_prediction(curr_mean, curr_cov, u(:,i), R, @motion_diff);
-  
-    % Store result for later 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);
-
-% ## Evaluate full UKF with measurements: 
-curr_mean = [0; 0; 0];
-curr_cov = diag([0, 0, 0]);
-ukf_mean = curr_mean;
-ukf_cov = reshape(curr_cov, 9, 1);
-for i = 1:size(u,2)
-    [curr_mean, curr_cov] = ukf_prediction(curr_mean, curr_cov, u(:,i), R, @motion_diff);
-    [curr_mean, curr_cov] = ukf_correction(curr_mean, curr_cov, z_meas(:,i), Q, @measurement);
-    
-    % Store result for later plotting
-    ukf_mean = [ukf_mean, curr_mean];
-    ukf_cov  = [ukf_cov, reshape(curr_cov, 9, 1)];
-end % for each u
-
-plot_trajectory_cov(x_true, ukf_mean, ukf_cov, dt);
-
-disp('final result WITH measurements')
-final_mean = curr_mean
-final_cov = curr_cov
-final_err = final_mean - x_true(:,end);
-err_pos = norm(final_err(1:2))
-err_yaw = abs(final_err(3))
\ No newline at end of file
diff --git a/mr/ub9/loc_jp/measurement.m b/mr/ub9/loc_jp/measurement.m
deleted file mode 100644
index 73a8a17..0000000
--- a/mr/ub9/loc_jp/measurement.m
+++ /dev/null
@@ -1,29 +0,0 @@
-function [ z H ] = measurement( x )
-%MEASUREMENT Measurement Model:
-%   Measure the robot's distance from several landmarks.
-
-% landmarks:
-l = [0, -1; -3, -2; 0, 6; -3, 6]';
-
-nl = size(l,2);
-nx = size(x,3);
-
-z = zeros(nl,1,nx);
-H = zeros(nl,3);
-
-pos = x(1:2,:,:);
-for i = 1:nl
-    diff = pos - repmat(l(:,i),1,1,size(pos,3));
-    z(i,:,:) = sqrt(sum(diff.*diff,1));
-end % for
-
-% EKF case
-if size(x,3) == 1
-    for i = 1:nl
-        diff = pos - l(:,i);
-        H(i,:) = [ diff(1)/(z(i,1))^(1/2), diff(2)/(z(i,1))^(1/2), 0;];
-    end % for
-end 
-
-end
-
diff --git a/mr/ub9/loc_jp/motion_diff.m b/mr/ub9/loc_jp/motion_diff.m
deleted file mode 100644
index 1e5e8f9..0000000
--- a/mr/ub9/loc_jp/motion_diff.m
+++ /dev/null
@@ -1,33 +0,0 @@
-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);
-
-r = l*(sl + sr)/(2*(sr-sl));
-
-G = eye(3);
-if r == Inf
-    % Special case: Robot is driving straight:
-    d = (sl+sr)/2;
-    xnew = x + [d*cos(theta); ...
-                d*sin(theta); ...
-                zeros(1,1,size(x,3))];
-else
-	dTh = repmat((sr - sl)/l,1,1,size(x,3));
-	xnew = x + [r*sin(theta + dTh) - r*sin(theta); ...
-               -r*cos(theta + dTh) + r*cos(theta); ...
-                dTh];
-            
-    if (size(x,3) == 1)
-        G = [1, 0, r*(cos(theta + dTh) - cos(theta)); ...
-            0, 1, r*(sin(theta + dTh) - sin(theta)); ...
-            0, 0, 1];
-    end
-end % if
-
-end
-
diff --git a/mr/ub9/loc_jp/pf_prediction.m b/mr/ub9/loc_jp/pf_prediction.m
deleted file mode 100644
index 430ce9d..0000000
--- a/mr/ub9/loc_jp/pf_prediction.m
+++ /dev/null
@@ -1,14 +0,0 @@
-function [ xnew ] = pf_prediction( x, u, R, motion)
-%PF_PREDICTION Particle Filter prediction step
-
-% Number of particles:
-m = size(x, 3);
-
-% YOUR CODE STARTS HERE:
-
-xnew = x;
-
-% YOUR CODE ENDS HERE:
-    
-end
-
diff --git a/mr/ub9/loc_jp/pf_resample.m b/mr/ub9/loc_jp/pf_resample.m
deleted file mode 100644
index e356eee..0000000
--- a/mr/ub9/loc_jp/pf_resample.m
+++ /dev/null
@@ -1,22 +0,0 @@
-function [ nextx ] = pf_resample( currx, w )
-%PF_RESAMPLE Basic resampling
-
-% number of samples:
-M = size(currx, 3);
-
-% Redraw particles:
-nextx = zeros(3, 1, M);
-
-% Cumulative sum of weights:
-cum = cumsum(w);
-
-for p = 1:M
-    r = rand();
-    % Find first index for which the cumulative sum is >= r. If this ever
-    % fails due to unmerical inaccuracies, we should take the last element.
-    ind = min(find (cum >= r, 1, 'first'), M);
-    nextx(:,:,p) = currx(:,:,ind);
-end
-
-end
-
diff --git a/mr/ub9/loc_jp/pf_resample_sys.m b/mr/ub9/loc_jp/pf_resample_sys.m
deleted file mode 100644
index fb01f81..0000000
--- a/mr/ub9/loc_jp/pf_resample_sys.m
+++ /dev/null
@@ -1,11 +0,0 @@
-function [ nextx ] = pf_resample_sys( currx, w )
-%PF_RESAMPLE_LV Systematic or low variance resampling
-
-% YOUR CODE STARTS HERE:
-
-nextx = currx;
-
-% YOUR CODE END HERE
-
-end
-
diff --git a/mr/ub9/loc_jp/pf_reweight.m b/mr/ub9/loc_jp/pf_reweight.m
deleted file mode 100644
index f450fcf..0000000
--- a/mr/ub9/loc_jp/pf_reweight.m
+++ /dev/null
@@ -1,14 +0,0 @@
-function [ wnew ] = pf_reweight( x, w, z, Q, measurement )
-%PF_CORRECTION Particle Filter correction step
-
-nSamples = size(x, 3);
-
-% Reweighting based on expected and actual measurement:
-z_exp = measurement(x);
-
-tmp = num2cell(z_exp,1);
-wnew = w.*reshape(cellfun(@(x) mvnpdf(x, z, Q) ,tmp), nSamples, 1, 1);
-wnew = wnew / sum(wnew);
-
-end
-
diff --git a/mr/ub9/loc_jp/plot_trajectory_cov.m b/mr/ub9/loc_jp/plot_trajectory_cov.m
deleted file mode 100644
index a9d98f2..0000000
--- a/mr/ub9/loc_jp/plot_trajectory_cov.m
+++ /dev/null
@@ -1,45 +0,0 @@
-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/ub9/loc_jp/plot_trajectory_particles.m b/mr/ub9/loc_jp/plot_trajectory_particles.m
deleted file mode 100644
index 48387ae..0000000
--- a/mr/ub9/loc_jp/plot_trajectory_particles.m
+++ /dev/null
@@ -1,45 +0,0 @@
-function [  ] = plot_trajectory_particles( xtrue, x, w, dt )
-%PLOT_TRAJECTORY_PARTICLES Summary of this function goes here
-%   Detailed explanation goes here
-
-% 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.
-% Normalize weights
-s = sum(w,3);
-w = w ./ repmat(s,1,1,size(w,3));
-mux = sum(x.*repmat(w,3,1,1),3);
-plot(mux(1,:), mux(2,:), 'b')
-
-% Only at some times: Draw all particles
-ptime = 4/dt; % Once in 4 seconds
-
-xdraw = x(:,1:ptime:end,:);
-
-% Draw particles
-nSamples = size(x,3);
-for i = 1:size(xdraw,2)
-    currx = xdraw(:,i,:);
-    quiver(reshape(currx(1,:,:),nSamples,1,1), ...
-        reshape(currx(2,:,:),nSamples,1,1), ...
-        cos(reshape(currx(3,:,:),nSamples,1,1)), ...
-        sin(reshape(currx(3,:,:),nSamples,1,1)));        
-end
-
-% Description & legend
-axis equal
-xlabel('x in m')
-ylabel('y in m')
-title('robot trajectory with uncertainty estimates')
-legend('true trajectory', ...
-       'estimate (mean)', ...
-       'location', 'SouthEast');
-
-
-end
-
diff --git a/mr/ub9/loc_jp/ukf_correction.m b/mr/ub9/loc_jp/ukf_correction.m
deleted file mode 100644
index ea3677f..0000000
--- a/mr/ub9/loc_jp/ukf_correction.m
+++ /dev/null
@@ -1,12 +0,0 @@
-function [ mean, cov ] = ukf_correction( pred_mean, pred_cov, z, Q, measurement )
-%UKF_CORRECTION UKF correction step
-
-% YOUR CODE STARTS HERE:
-
-mean = pred_mean;
-cov = pred_cov;
-
-% YOUR CODE ENDS HERE:
-
-end
-
diff --git a/mr/ub9/loc_jp/ukf_estimate_normal.m b/mr/ub9/loc_jp/ukf_estimate_normal.m
deleted file mode 100644
index e3a82ac..0000000
--- a/mr/ub9/loc_jp/ukf_estimate_normal.m
+++ /dev/null
@@ -1,28 +0,0 @@
-function [ mu, cov ] = ukf_estimate_normal( S, w )
-%UKF_ESTIMATE_NORMAL Estimate mean, covariance from set of sigma points
-
-% Dimension (3 for 2D localization)
-dim = size(S,1);
-
-% Number of sigma points (= number of weights):
-n = size(w,1); 
-
-mu = zeros(dim,1);
-cov = zeros(dim,dim);
-
-% YOUR CODE STARTS HERE:
-for i=1:n
-    mu=mu+w(i)*S(i);
-end
-
-for i=1:n
-   cov=cov+w(i)*(S(i)-mu)*(S(i)-mu)'; 
-end
-w
-S
-mu 
-cov
-% YOUR CODE END HERE:
-
-end
-
diff --git a/mr/ub9/loc_jp/ukf_prediction.m b/mr/ub9/loc_jp/ukf_prediction.m
deleted file mode 100644
index a01e9c0..0000000
--- a/mr/ub9/loc_jp/ukf_prediction.m
+++ /dev/null
@@ -1,20 +0,0 @@
-function [ newmean, newcov ] = ukf_prediction( mean, cov, u, R, motion)
-%UKF_PREDICTION Computes the UKF prediction step
-
-% YOUR CODE STARTS HERE:
-[S w]=ukf_sigma_points(mean,cov,1/3);
-n=size(w,1);
-Snew=zeros(size(S));
-
-for i=1:n
-    [Snew(:,i), G]=motion(S(:,i),u);
-end
-
-[newmean, newcov]=ukf_estimate_normal(Snew,w);
-newcov=newcov+R;
-
-
-% YOUR CODE ENDS HERE:
-
-end
-
diff --git a/mr/ub9/loc_jp/ukf_sigma_points.m b/mr/ub9/loc_jp/ukf_sigma_points.m
deleted file mode 100644
index 6e7218d..0000000
--- a/mr/ub9/loc_jp/ukf_sigma_points.m
+++ /dev/null
@@ -1,39 +0,0 @@
-function [ S w] = ukf_sigma_points( mu, cov, w0 )
-%UKF_SIGMA_POINTS Compute sigma points from mean and covariance matrix.
-
-% Use default value for w0 if it is not provided as a parameter:
-if nargin < 3
-    w0 = 1/3;
-end
-
-nx = size(mu,1); % Dimension (3 for 2D localization)
-
-n = 2*nx + 1;    % n sigma points
-
-% Store sigma points in (nx x n) matrix
-% Sigma point i is S(:,i)
-S = zeros(nx, n);
-
-% Store weights in (n x 1) matrix:
-% Weight of sigma point i is w(i);
-w = zeros(n,1);
-
-% YOUR CODE STARTS HERE
-w(1)=w0;
-S(:,1)=mu;
-
-offset=sqrt(nx/(1-w0))*sqrtm(cov);
-weight=(1-w0)/(2*nx);
-
-for i=2:nx+1
-    S(:,i)=mu+offset(:,i-1);
-    w(i)=weight;
-    S(:,i+nx)=mu-offset(:,i-1);
-    w(i+nx)=weight;
-end
-
-
-% YOR CODE ENDS HERE
-
-end
-