Estimate essential matrix from corresponding points in a pair of images

returns the 3-by-3 essential matrix, `E`

= estimateEssentialMatrix(`matchedPoints1`

,`matchedPoints2`

,`cameraParams`

)`E`

, using the M-estimator sample
consensus (MSAC) algorithm. The input points can be *M*-by-2 matrices of
*M* number of [*x*,*y*] coordinates, or a
`KAZEPoints`

, `SURFPoints`

, `MSERRegions`

, `BRISKPoints`

, or `cornerPoints`

object. The
`cameraParams`

object contains the parameters of the camera used to take
the images.

returns the essential matrix relating two images taken by different cameras.
`E`

= estimateEssentialMatrix(`matchedPoints1`

,`matchedPoints2`

,`cameraParams1`

,`cameraParams2`

)`cameraParams1`

and `cameraParams2`

are `cameraParameters`

objects containing the parameters of camera 1 and camera 2 respectively.

`[`

additionally
returns an `E`

,`inliersIndex`

]
= estimateEssentialMatrix(`matchedPoints1`

,`matchedPoints2`

)*M*-by-1 logical vector, `inliersIndex`

,
used to compute the essential matrix. The function sets the elements
of the vector to `true`

when the corresponding point
was used to compute the fundamental matrix. The elements are set to `false`

if
they are not used.

`[`

additionally
returns a status code to indicate the validity of points.`E`

,`inliersIndex`

,`status`

]
= estimateEssentialMatrix(`matchedPoints1`

,`matchedPoints2`

)

`[`

uses
additional options specified by one or more Name,Value pair
arguments.`E`

,`inliersIndex`

,`status`

]
= estimateEssentialMatrix(___,`Name,Value`

)

Use `estimateEssentialMatrix`

when you know
the camera intrinsics. You can obtain the intrinsics using the **Camera Calibrator** app. Otherwise,
you can use the `estimateFundamentalMatrix`

function
that does not require camera intrinsics. The fundamental matrix cannot
be estimated from coplanar world points.

[1] Kukelova, Z., M. Bujnak, and T. Pajdla *Polynomial
Eigenvalue Solutions to the 5-pt and 6-pt Relative Pose Problems.* Leeds,
UK: BMVC, 2008.

[2] Nister, D.. “An Efficient Solution
to the Five-Point Relative Pose Problem.” *IEEE Transactions
on Pattern Analysis and Machine Intelligence.*Volume 26,
Issue 6, June 2004.

[3] 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.