jason216
2009-04-15, 16:03
各位大侠，本人要做一个三维轮廓测量系统的数据融合，在网上下载到了数据融合的ICP算法的源代码，但是不知道输入变量和输出变量是什么，哪位大侠能帮我看看，我只是想知道输入的变量是怎样的格式。。。
以下是代码，前面有这个函数的说明，我用坐标的矩阵输入，可输出不对
谢谢各位大侠！！！
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鰉 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)
。。。。后面的太长了，省略
以下是代码，前面有这个函数的说明，我用坐标的矩阵输入，可输出不对
谢谢各位大侠！！！
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鰉 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)
。。。。后面的太长了，省略