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update Matlab scripts to work after changes in gtsam

release/4.3a0
Viorela Ila 2009-11-12 20:41:05 +00:00
parent 6252ff2497
commit b43304aff8
8 changed files with 179 additions and 31 deletions
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2
matlab/CHECK.m
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@ -1,4 +1,4 @@
function CEHCK(name,assertion)
function CHECK(name,assertion)
if (assertion~=1)
error(['CHECK ' name ' fails']);

43
matlab/createLinearFactorGraph.m
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@ -6,36 +6,57 @@ c = createNoisyConfig();
% Create
fg = GaussianFactorGraph;
sigma1=.1;
% prior on x1
A11=[10 0; 0 10];
b = - c.get('x1')/0.1;
A11=eye(2);
b = - c.get('x1');
<<<<<<< .mine
f1 = LinearFactor('x1', A11, b, sigma1);
=======
f1 = GaussianFactor('x1', A11, b);
>>>>>>> .r1017
fg.push_back(f1);
% odometry between x1 and x2
A21=[-10 0; 0 -10];
A22=[ 10 0; 0 10];
b = [2;-1];
sigma2=.1;
<<<<<<< .mine
A21=-eye(2);
A22=eye(2);
b = [.2;-.1];
f2 = LinearFactor('x1', A21, 'x2', A22, b,sigma2);
=======
f2 = GaussianFactor('x1', A21, 'x2', A22, b);
>>>>>>> .r1017
fg.push_back(f2);
% measurement between x1 and l1
A31=[-5 0; 0 -5];
A32=[ 5 0; 0 5];
b = [0;1];
sigma3=.2;
A31=-eye(2);
A33=eye(2);
b = [0;.2];
<<<<<<< .mine
f3 = LinearFactor('x1', A31, 'l1', A33, b,sigma3);
=======
f3 = GaussianFactor('x1', A31, 'l1', A32, b);
>>>>>>> .r1017
fg.push_back(f3);
% measurement between x2 and l1
A41=[-5 0; 0 -5];
A42=[ 5 0; 0 5];
b = [-1;1.5];
sigma4=.2;
A42=-eye(2);
A43=eye(2);
b = [-.2;.3];
<<<<<<< .mine
f4 = LinearFactor('x2', A42, 'l1', A43, b,sigma4);
=======
f4 = GaussianFactor('x2', A41, 'l1', A42, b);
>>>>>>> .r1017
fg.push_back(f4);
end

2
matlab/createZeroDelta.m
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@ -3,7 +3,7 @@ function c = createZeroDelta()
v_x1 = [0; 0];
v_x2 = [0; 0];
v_l1 = [0; 0];
c = FGConfig();
c = VectorConfig();
c.insert('x1', v_x1);
c.insert('x2', v_x2);
c.insert('l1', v_l1);

6
matlab/create_random_landmarks.m
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@ -1,6 +1,8 @@
% Christian Potthast
% Create a map with random landmarks
function map = create_random_landmarks(numberOfLandmarks)
function map = create_random_landmarks(numberOfLandmarks, mappingArea)
map = rand(2,numberOfLandmarks)*100;
points = rand(2,numberOfLandmarks);
map=[points(1,:)*mappingArea(1);points(2,:)*mappingArea(2)];

26
matlab/example.m
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@ -3,21 +3,21 @@
clear;
n = 100;
m = 20;
n = 1000;
m = 200;
% Set up the map
map = create_random_landmarks(n);
map = create_random_landmarks(n,[1000,1000]);
figure(1);clf;
plot(map(1,:), map(2,:),'g.'); hold on;
% have the robot move in this world
trajectory = random_walk([0.1,0.1],5,m);
plot(trajectory(1,:),trajectory(2,:),'b+');
axis([0 100 0 100]);axis square;
axis([0 1000 0 1000]);axis square;
% Check visibility and plot this on the problem figure
visibility = create_visibility(map, trajectory,10);
visibility = create_visibility(map, trajectory,50);
gplot(visibility,[map trajectory]');
figure(2);clf;
spy(visibility)
@ -38,12 +38,20 @@ ord = create_ordering(n,m);
% show the matrix
figure(3); clf;
[A,b] = linearFactorGraph.matrix(ord);
spy(A);
[A_dense,b] = linearFactorGraph.matrix(ord);
%spy(A);
A=sparse(A_dense);
% eliminate with that ordering
ck = cputime;
BayesNet = linearFactorGraph.eliminate(ord);
time_gtsam = cputime - ck
ckqr = cputime;
R = qr(A);
time_qr = cputime - ckqr
%time_gtsam=[time_gtsam,(cputime-ck)]
% show the eliminated matrix
figure(4); clf;
[R,d] = BayesNet.matrix();
@ -57,4 +65,4 @@ figure(5);clf;
plot_config(optimal,n,m);hold on
plot(trajectory(1,:),trajectory(2,:),'b+');
plot(map(1,:), map(2,:),'g.');
axis([0 100 0 100]);axis square;
axis([0 1000 0 1000]);axis square;

6
matlab/testConditionalGaussian.m
View File

@ -8,13 +8,15 @@ A1 =[1 2; 3 4];
A2 = [6 0.2;8 0.4];
R = [0.1 0.3; 0.0 0.34];
d=[0.2;0.5];
tau=[1;.34];
cg = ConditionalGaussian(d, R, 'x1', A1, 'l1', A2);
cg = ConditionalGaussian('x',d, R, 'x1', A1, 'l1', A2, tau);
sx1 = [0.2;0.5];
sl1 = [0.5;0.8];
solution = FGConfig;
%solution = FGConfig;
solution.insert('x1', sx1);
solution.insert('l1', sl1);

13
matlab/test_time.m
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@ -3,7 +3,7 @@
clear;
%Parameters
noRuns=100;
noRuns=5;
steps=1;
m = 5;
velocity=1;
@ -28,7 +28,7 @@ for steps=1:noRuns
visibility = create_visibility(map, trajectory,visibilityTh);
%gplot(visibility,[map trajectory]');
steps
% simulate the measurements
measurement_sigma = 1;
odo_sigma = 0.1;
@ -46,9 +46,12 @@ for steps=1:noRuns
% show the matrix
% figure(3); clf;
[A_dense,b] = linearFactorGraph.matrix(ord);
A=sparse(A_dense);
size(A)
%[A_dense,b] = linearFactorGraph.matrix(ord);
%A=sparse(A_dense);
ijs = linearFactorGraph.sparse(ord);
A=sparse(ijs(1,:),ijs(2,:),ijs(3,:));
%spy(A);
%time qr
ck=cputime;

112
matlab/test_time_new.m Normal file
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@ -0,0 +1,112 @@
% Set up a small SLAM example in MATLAB to test the execution time
clear;
%Parameters
noRuns=5;
steps=50;
m = 5*steps;
velocity=1*steps;
time_qr=[];
time_gtsam=[];
% for steps=1:noRuns
%
figure(1);clf;
% robot moves in the world
trajectory = walk([0.1,0.1],velocity,m);
mappingArea=max(trajectory,[],2);
plot(trajectory(1,:),trajectory(2,:),'b+'); hold on;
visibilityTh=sqrt(mappingArea(1)^2+mappingArea(2)^2)/m; %distance between poses
% Set up the map
map = create_landmarks(visibilityTh, mappingArea,steps);
plot(map(1,:), map(2,:),'g.');
axis([0 mappingArea(1) 0 mappingArea(2)]); axis square;
n=size(map,2);
% Check visibility and plot this on the problem figure
visibilityTh=visibilityTh+steps;
visibility = create_visibility(map, trajectory,visibilityTh);
gplot(visibility,[map trajectory]');
% simulate the measurements
measurement_sigma = 1;
odo_sigma = 0.1;
[measurements, odometry] = simulate_measurements(map, trajectory, visibility, measurement_sigma, odo_sigma);
% % create a configuration of all zeroes
config = create_config(n,m);
% create the factor graph
linearFactorGraph = create_linear_factor_graph(config, measurements, odometry, measurement_sigma, odo_sigma, n);
%
% create an ordering
%ord = create_good_ordering(n,m,measurements);
ord = create_ordering(n,m);
% show the matrix
% figure(3); clf;
%[A_dense,b] = linearFactorGraph.matrix(ord);
%A=sparse(A_dense);
%sparse matrix !!!
ijs = linearFactorGraph.sparse(ord);
A=sparse(ijs(1,:),ijs(2,:),ijs(3,:));
%spy(A);
%time qr
runs=1;
ck_qr=cputime;
for i=1:runs
R_qr = qr(A);
end
time_qr=(cputime-ck_qr)/runs
%time_qr=[time_qr,(cputime-ck)];
%figure(2)
%clf
%spy(R_qr);
% eliminate with that ordering
%time gt_sam
% for i=1:runs+10
% if i==11
% ck_gt=cputime;
% end
% BayesNet = linearFactorGraph.eliminate(ord);
% end
ck_gt=cputime;
for i=1:runs+10
BayesNet = linearFactorGraph.eliminate(ord);
end
time_gtsam=(cputime-ck_gt)/runs
%time_gtsam=[time_gtsam,(cputime-ck)];
% clear trajectory visibility linearFactorGraph measurements odometry;
% m = m+5;
% velocity=velocity+1;
% end
% %time_qr=time_qr/noRuns
% plot(time_qr,'r');hold on;
% plot(time_gtsam,'b');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % show the eliminated matrix
% figure(4); clf;
% [R,d] = BayesNet.matrix();
% spy(R);
%
% % optimize in the BayesNet
% optimal = BayesNet.optimize;
%
% % plot the solution
% figure(5);clf;
% plot_config(optimal,n,m);hold on
% plot(trajectory(1,:),trajectory(2,:),'b+');
% plot(map(1,:), map(2,:),'g.');
% axis([0 10 0 10]);axis square;