Re-organized MATLAB code

release/4.3a0
Frank Dellaert 2010-02-22 14:35:57 +00:00
parent 65cbff6af6
commit f3a24c5c88
35 changed files with 0 additions and 362 deletions

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function addSimulatedConstraint(points,angles,sd,id1,id2,graph)
% addSimulatedConstraint: create a simulated measurement with noise
% standard deviations sd and add it to graph
key1 = sprintf('x%d', id1);
key2 = sprintf('x%d', id2);
% ground truth
delta_x = points(id1,:) - points(id2,:);
delta_angle = angles(id1) - angles(id2);
noisy = Pose2(delta_x(1) + sd(1)*randn, delta_x(2) + sd(2)*randn, delta_angle + sd(3)*randn);
% create factor
factor=Pose2Factor(key1,key2,noisy,diag(sd.*sd));
% add it to the graph
graph.push_back(factor);

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% find a bottom to up ordering given the tree structure {pred} returned by matlab's graphminspantree
function [ordering] = bottom_up_ordering(pred)
%% compute the levels of the nodes
parents = [0];
node_levels = zeros(length(pred), 1);
current_level = 1;
while ~isempty(parents)
parents = find(ismember(pred, parents));
node_levels(parents) = current_level;
current_level = current_level + 1;
end
[~, node_order] = sort(node_levels, 'descend'); % the order of the nodes in leaves-to-root order
ordering = Ordering();
for i = 1:length(node_order)
ordering.push_back(sprintf('x%d', node_order(i)));
end
end

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%-----------------------------------------------------------------------
% frank01.m: try conjugate gradient on our example graph
%-----------------------------------------------------------------------
% get matrix form H and z
fg = createGaussianFactorGraph();
ord = Ordering;
ord.push_back('x1');
ord.push_back('x2');
ord.push_back('l1');
[H,z] = fg.matrix(ord);
% form system of normal equations
A=H'*H
b=H'*z
% k=0
x = zeros(6,1)
g = A*x-b
d = -g
for k=1:5
alpha = - (d'*g)/(d'*A*d)
x = x + alpha*d
g = A*x-b
beta = (d'*A*g)/(d'*A*d)
d = -g + beta*d
end
% Do gradient descent
% fg2 = createGaussianFactorGraph();
% zero = createZeroDelta();
% actual = fg2.gradientDescent(zero);
% CHECK(assert_equal(expected,actual,1e-2));
% Do conjugate gradient descent
% actual2 = fg2.conjugateGradientDescent(zero);
% CHECK(assert_equal(expected,actual2,1e-2));

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% load the Toro 2D dataset and build pose graph

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% frank03: create Beijing matrices in a cleaner way
load beijing.mat;
load beijing_angles.mat;
load beijing_graph.mat;
n=size(points,1);
% create config or load it from file
if 0
load beijing_config.mat;
else
config=Pose2Config();
for j=1:n
if mod(j,1000) == 0, fprintf(1, 'adding node %d to config\n', j); end
pose=Pose2(points(j,1),points(j,2),angles(j));
key = sprintf('x%d', j);
config.insert(key,pose);
end
save('beijing_config.mat','config');
end
sd = [0.25;0.25;0.01];
% Build factor graph for entire graph
graph = Pose2Graph;
% First add tree constraints
[I J] = find(tree);
for k=length(edge_order):-1:1
edge = edge_order(k);
if mod(k,1000) == 0, fprintf(1, 'simulating constraint %d\n', k); end
addSimulatedConstraint(points,angles,sd,I(edge),J(edge),graph);
end
% Then add remaining constraints C
C=G-tree;
[I J] = find(C);
for k=1:length(I)
if mod(k,100) == 0, fprintf(1, 'simulating constraint %d\n', k); end
addSimulatedConstraint(points,angles,sd,I(k),J(k),graph);
end
% generate ordering
ordering = bottom_up_ordering(pred);

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% frank04: show matrices associated with Beijing simulation
load beijing.mat;
load beijing_angles.mat;
load beijing_graph.mat;
% put spanning tree 'tree' in correct order
T = tree(node_order,node_order);
% get loop closing constraints
C=G(node_order,node_order)-T;
close all
% plot on map
figure
gplot(C,points(node_order,:),'r')
hold on
gplot(T,points(node_order,:),'k')
axis equal
% show spanning tree, original graph, and loop closures
figure
spy(T,'k');
hold on
spy(C,'r');
figure
spy(T);
figure
spy(C);

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% frank05: show right pre-conditioning
% first run frank03 or other script to generate graph, config, and ordering
% linearize the non-linear factor graph
tic
LFG = graph.linearize_(config);
toc
tic
ijs = LFG.sparse(ordering);
A = sparse(ijs(1,:),ijs(2,:),ijs(3,:));
toc
figure(1)
spy(A);
% isolate the spanning tree part
A1=A(1:3*nnz(tree),:);
% add prior
figure(2)
spy(A1)
% calculate R1
tic
R1 = qr(A1,0);
toc
figure(3)
spy(R1)
% calculate R1
tic
R1 = chol(A1'*A1);
toc
figure(3)
spy(R1)
% calculate the entire R factor (expensive)
tic
R = qr(A,0);
toc
figure(4)
spy(R)

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load beijing.mat;
load beijing_angles.mat;
load beijing_graph.mat;
load beijing_config.mat;
cov = [ 0.25, 0, 0; 0, 0.25, 0; 0, 0, 0.01];
factors = Pose2Graph;
factors2 = Pose2Graph;
ord2 = Ordering();
[rows cols] = find(tree);
for i=length(edge_order):-1:1
if mod(i,500) == 0
fprintf(1, 'processing edge %d\n', i);
end
id1 = rows(edge_order(i));
id2 = cols(edge_order(i));
key1 = sprintf('x%d', id1);
key2 = sprintf('x%d', id2);
delta_x = points(id1,:) - points(id2,:);
delta_angle = angles(id1) - angles(id2);
measured = Pose2(delta_x(1), delta_x(2), delta_angle);
if pred(id1) == id2 || pred(id2) == id1 %% in the spanning tree
factor=Pose2Factor(key1,key2,measured, cov);
factors.push_back(factor);
else %% not in the spanning tree
factors2.push_back(Pose2Factor(key1,key2,measured, cov));
ord2.push_back(key1);
ord2.push_back(key2);
end
end
ord2.unique();
if 1
config=Pose2Config();
n=size(points,1);
for j=1:n
pose=Pose2(points(j,1),points(j,2),angles(j));
key = sprintf('x%d', j);
config.insert(key,pose);
if mod(j,1000) == 0
key
end
end
save('beijing_config.mat','config');
end
% Spanning tree with bottom-up ordering
ord = bottom_up_ordering(pred);
LFG=factors.linearize_(config);
ijs=LFG.sparse(ord);
A=sparse(ijs(1,:),ijs(2,:),ijs(3,:));
figure(1)
spy(A);
%save('beijing_factors.mat', 'factors');
% LFG2=factors2.linearize_(config);
% ijs2=LFG2.sparse(ord2);
% A2=sparse(ijs2(1,:),ijs2(2,:),ijs2(3,:));
% figure(2)
% spy(A2);
% show R factor
R = qr(A,0);
figure(3)
spy(R)
% show re-ordered R factor
% P = colamd(A);
% figure(4)
% spy(A(:,P))
% R = qr(A(:,P),0);
% figure(5)
% spy(R)

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load beijing_graph.mat;
ordering = bottom_up_ordering(pred);
ordering.print('ordering')

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load beijing.mat;
load beijing_angles.mat;
load beijing_graph.mat;
%load beijing_config.mat;
cov = [ 0.25, 0, 0; 0, 0.25, 0; 0, 0, 0.01];
factors = Pose2Graph;
factors2 = Pose2Graph;
ord = Ordering();
ord2 = Ordering();
for i=1:length(ways)
if mod(i,500) == 0
fprintf(1, 'processing way %d\n', i);
end
for j=1:length(ways{i})-1
id1 = ways{i}(j);
id2 = ways{i}(j+1);
key1 = sprintf('x%d', id1);
key2 = sprintf('x%d', id2);
delta_x = points(id1,:) - points(id2,:);
delta_angle = angles(id1) - angles(id2);
measured = Pose2(delta_x(1), delta_x(2), delta_angle);
if pred(id1) == id2 || pred(id2) == id1 %% in the spanning tree
factor=Pose2Factor(key1,key2,measured, cov);
factors.push_back(factor);
ord.push_back(key1);
ord.push_back(key2);
else %% not in the spanning tree
factors2.push_back(Pose2Factor(key1,key2,measured, cov));
ord2.push_back(key1);
ord2.push_back(key2);
end
end
end
ord.unique();
ord2.unique();
% ord.reverse();
% ord2.reverse();
if 0
config=Pose2Config();
n=size(points,1);
for j=1:n
pose=Pose2(points(j,1),points(j,2),angles(j));
key = sprintf('x%d', j);
config.insert(key,pose);
if mod(j,1000) == 0
key
end
end
save('beijing_config.mat','config');
end
amd_ord=factors.getOrdering_(); % does not work
LFG=factors.linearize_(config);
ijs=LFG.sparse(ord);
A=sparse(ijs(1,:),ijs(2,:),ijs(3,:));
figure(1)
spy(A);
%save('beijing_factors.mat', 'factors');
LFG2=factors2.linearize_(config);
ijs2=LFG2.sparse(ord2);
A2=sparse(ijs2(1,:),ijs2(2,:),ijs2(3,:));
figure(2)
spy(A2);
% show R factor
R = qr(A,0);
figure(3)
spy(R)
% show re-ordered R factor
P = colamd(A);
figure(4)
spy(A(:,P))
R = qr(A(:,P),0);
figure(5)
spy(R)