function [TR, TT] = icp(model,data,max_iter,min_iter,fitting,thres,init_flag,tes_flag,refpnt)
% ICP Iterative Closest Point Algorithm. Takes use of
% Delaunay tesselation of points in model.
%
% Ordinary usage:
%
% [R, T] = icp(model,data)
%
% ICP fit points in data to the points in model.
% Fit with respect to minimize the sum of square
% errors with the closest model points and data points.
%
% INPUT:
%
% model - matrix with model points, [Pm_1 Pm_2 ... Pm_nmod]
% data - matrix with data points, [Pd_1 Pd_2 ... Pd_ndat]
%
% OUTPUT:
%
% R - rotation matrix and
% T - translation vector accordingly so
%
% newdata = R*data + T .
%
% newdata are transformed data points to fit model
%
%
% Special usage:
%
% icp(model) or icp(model,tes_flag)
%
% ICP creates a Delaunay tessellation of points in
% model and save it as global variable Tes. ICP also
% saves two global variables ir and jc for tes_flag=1 (default) or
% Tesind and Tesver for tes_flag=2, which
% makes it easy to find in the tesselation. To use the global variables
% in icp, put tes_flag to 0.
%
%
% Other usage:
%
% [R, T] = icp(model,data,max_iter,min_iter,...
% fitting,thres,init_flag,tes_flag)
%
% INPUT:
%
% max_iter - maximum number of iterations. Default=104
%
% min_iter - minimum number of iterations. Default=4
%
% fitting - =2 Fit with respect to minimize the sum of square errors. (default)
% alt. =[2,w], where w is a weight vector corresponding to data.
% w is a vector of same length as data.
% Fit with respect to minimize the weighted sum of square errors.
% =3 Fit with respect to minimize the sum to the amount 0.95
% of the closest square errors.
% alt. =[3,lambda], 0.0<lambda<=1.0, (lambda=0.95 default)
% In each iteration only the amount lambda of the closest
% points will affect the translation and rotation.
% If 1<lambda<=size(data,2), lambda integer, only the number lambda
% of the closest points will affect the translation and
% rotation in each iteration.
%
% thres - error differens threshold for stop iterations. Default 1e-5
%
% init_flag - =0 no initial starting transformation
% =1 transform data so the mean value of
% data is equal to mean value of model.
% No rotation. (init_flag=1 default)
%
% tes_flag - =0 No new tesselation has to be done. There
% alredy exists one for the current model points.
% =1 A new tesselation of the model points will
% be done. (default)
% =2 A new tesselation of the model points will
% be done. Another search strategy than tes_flag=1
% =3 The closest point will be find by testing
% all combinations. No Delaunay tesselation will be done.
%
% refpnt - (optional) (An empty vector is default.) refpnt is a point corresponding to the
% set of model points wich correspondig data point has to be find.
% How the points are weighted depends on the output from the
% function weightfcn found in the end of this m-file. The input in weightfcn is the
% distance between the closest model point and refpnt.
%
% To clear old global tesselation variables run: "clear global Tes ir jc" (tes_flag=1)
% or run: "clear global Tes Tesind Tesver" (tes_flag=2) in Command Window.
%
% m-file can be downloaded for free at
% http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=12627&objectType=FILE
%
% icp version 1.4
%
% written by Per Bergstr�m 2007-03-07
if nargin<1
error('To few input arguments!');
elseif or(nargin==1,nargin==2)
bol=1;
refpnt=[];
if nargin==2
if isempty(data)
tes_flag=1;
elseif isscalar(data)
tes_flag=data;
if not(tes_flag==1 | tes_flag==2)
tes_flag=1;
end
else
bol=0;
end
else
tes_flag=1;
end
if bol
global MODEL
if isempty(model)
error('Model can not be an empty matrix.');
end
if (size(model,2)<size(model,1))
MODEL=model';
TR=eye(size(model,2));
TT=zeros(size(model,2),1);
else
MODEL=model;
TR=eye(size(model,1));
TT=zeros(size(model,1),1);
end
if (size(MODEL,2)==size(MODEL,1))
error('This icp method demands the number of MODEL points to be greater then the dimension.');
end
icp_struct(tes_flag);
return
end
end
global MODEL DATA TR TT
if isempty(model)
error('Model can not be an empty matrix.');
end
if (size(model,2)<size(model,1))
MODEL=model';
else
MODEL=model;
end
if (size(data,2)<size(data,1))
data=data';
DATA=data;
else
DATA=data;
end
if size(DATA,1)~=size(MODEL,1)
error('Different dimensions of DATA and MODEL!');
end
if nargin<9
refpnt=[];
if nargin<8
tes_flag=1;
if nargin<7
init_flag=1;
if nargin<6
thres=1e-5; % threshold to icp iterations
if nargin<5
fitting=2; % fitting method
if nargin<4
min_iter=4; % min number of icp iterations
if nargin<3
max_iter=104; % max number of icp iterations
end
end
end
end
end
end
elseif nargin>9
warning('Too many input arguments!');
end
if isempty(tes_flag)
tes_flag=1;
elseif not(tes_flag==0 | tes_flag==1 | tes_flag==2 | tes_flag==3)
init_flag=1;
warning('init_flag has been changed to 1');
end
if and((size(MODEL,2)==size(MODEL,1)),tes_flag~=0)
error('This icp method demands the number of model points to be greater then the dimension.');
end
if isempty(min_iter)
min_iter=4;
end
if isempty(max_iter)
max_iter=100+min_iter;
end
if max_iter<min_iter;
max_iter=min_iter;
warning('max_iter<min_iter , max_iter has been changed to be equal min_iter');
end
if min_iter<0;
min_iter=0;
warning('min_iter<0 , min_iter has been changed to be equal 0');
end
if isempty(thres)
thres=1e-5;
elseif thres<0
thres=abs(thres);
warning('thres negative , thres have been changed to -thres');
end
if isempty(fitting)
fitting=2;
elseif fitting(1)==2
[fi1,fi2]=size(fitting);
lef=max([fi1,fi2]);
if lef>1
if fi1<fi2
fitting=fitting';
end
if lef<(size(data,2)+1)
warning('Illegeal size of fitting! Unweighted minimization will be used.');
fitting=2;
elseif min(fitting(2:(size(data,2)+1)))<0
warning('Illegeal value of the weights! Unweighted minimization will be used.');
fitting=2;
elseif max(fitting(2:(size(data,2)+1)))==0
warning('Illegeal values of the weights! Unweighted minimization will be used.');
fitting=2;
else
su=sum(fitting(2:(size(data,2)+1)));
fitting(2:(size(data,2)+1))=fitting(2:(size(data,2)+1))/su;
thres=thres/su;
end
end
elseif fitting(1)==3
if length(fitting)<2
fitting=[fitting,round(0.95*size(data,2))];
elseif fitting(2)>1
if fitting(2)>floor(fitting(2))
f
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