113 lines
2.8 KiB
Matlab
113 lines
2.8 KiB
Matlab
% 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;
|