文件名称:IterativeClosestPointMethod
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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.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model - matrix with model points,
data - matrix with data points,
OUTPUT:
R - rotation matrix and
T - translation vector accordingly
so
newdata = R*data + T .
newdata are transformed data points to fit model
see help icp for more information
-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.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model- matrix with model points,
data- matrix with data points,
OUTPUT:
R- rotation matrix and
T- translation vector accordingly
so
newdata = R*data+ T .
newdata are transformed data points to fit model
see help icp for more information
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model - matrix with model points,
data - matrix with data points,
OUTPUT:
R - rotation matrix and
T - translation vector accordingly
so
newdata = R*data + T .
newdata are transformed data points to fit model
see help icp for more information
-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.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model- matrix with model points,
data- matrix with data points,
OUTPUT:
R- rotation matrix and
T- translation vector accordingly
so
newdata = R*data+ T .
newdata are transformed data points to fit model
see help icp for more information
(系统自动生成,下载前可以参看下载内容)
下载文件列表
Iterative Closest Point Method .txt