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Fit cylinder to 3-D point cloud

`model = pcfitcylinder(ptCloudIn,maxDistance)`

`model = pcfitcylinder(ptCloudIn,maxDistance,referenceVector)`

`model = pcfitcylinder(ptCloudIn,maxDistance,referenceVector,maxAngularDistance)`

```
[model,inlierIndices,outlierIndices]
= pcfitcylinder(ptCloudIn,maxDistance)
```

```
[___,meanError] =
pcfitcylinder(ptCloudIn,maxDistance)
```

`[___] = pcfitcylinder(___,Name,Value)`

fits
a cylinder to a point cloud with a maximum allowable distance from
an inlier point to the cylinder. This function uses the M-estimator
SAmple Consensus (MSAC) algorithm to find the cylinder.`model`

= pcfitcylinder(`ptCloudIn`

,`maxDistance`

)

fits
a cylinder to the point cloud with additional orientation constraints
specified by the 1-by-3 reference orientation input vector.`model`

= pcfitcylinder(`ptCloudIn`

,`maxDistance`

,`referenceVector`

)

additionally
specifies the maximum allowed absolute angular distance.`model`

= pcfitcylinder(`ptCloudIn`

,`maxDistance`

,`referenceVector`

,`maxAngularDistance`

)

`[`

additionally
returns linear indices to the inlier and outlier points in the point
cloud input.`model`

,`inlierIndices`

,`outlierIndices`

]
= pcfitcylinder(`ptCloudIn`

,`maxDistance`

)

`[___,`

additionally
returns the mean error of the distance of the inlier points to the
model.`meanError`

] =
pcfitcylinder(`ptCloudIn`

,`maxDistance`

)

`[___] = pcfitcylinder(___,`

uses
additional options specified by one or more `Name,Value`

)`Name,Value`

pair
arguments.

The function returns a geometric model that describes the cylinder. This function uses the M-estimator SAmple Consensus (MSAC) algorithm to find the cylinder. The MSAC algorithm is a variant of the RANdom SAmple Consensus (RANSAC) algorithm.

The fitting algorithm for the `pcfitcylinder`

function
requires point cloud normals. Therefore, if the `Normal`

property
for the input point cloud is empty, the function fills it. When the
function fills the `Normal`

property, it uses six points
to fit the local cylinder. If six points do not work and the fitting
fails, consider calling the `pcnormals`

function
which enables you to select the number of points to use.

[1] Torr, P. H. S., and A. Zisserman. “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry.” Computer Vision and Image Understanding. Volume 78, Issue 1, April 2000, pp. 138-156.

`affine3d`

| `cylinderModel`

| `pcdenoise`

| `pcfitplane`

| `pcfitsphere`

| `pcmerge`

| `pcplayer`

| `pcread`

| `pcregistericp`

| `pcshow`

| `pctransform`

| `pcwrite`

| `planeModel`

| `pointCloud`