Articulated structured light based-laparoscope

1. A method of using a structured-light based endoscope, comprising:

capturing at least one real-time 2D image of at least a portion of said field of view using a camera of an endoscope;

illuminating in real time at least a portion of said at least one object within at least a portion of said field of view with a structured light pattern;

detecting light reflected from said field of view;

from said light reflected from said field of view, generating said 3D image of said field of view and calculating 3D locations of points on a surface of said object, wherein said 3D image is constructable from said detected light reflected from said field of view and said structured light pattern; and

real-time locating the 3D spatial position at any given time t of at least one of the endoscope and a surgical tool;

causing the system to generate a user alert if a distance between the surface and said at least one of the endoscope and surgical tool, as determined using the 3D spatial position, falls below a predetermined distance;

wherein the method further comprises constructing said 3D image by calculating world coordinates of at least one point on said at least one object using the following equation:

α

_

=

n

T

⁢

x

~

p

n

T

⁢

R

p

⁢

v

c

.

where n T is the transpose of the normal to the plane defined by the stripe ID x p , p =x p +[δx p , 0, f p ] T is the perturbed stripe ID x p , R p is the rotation matrix defining the transformation between the world coordinate system and the projector coordinate system and v c is the direction of the ray between the stripe ID and the object point.

2. The method of claim 1 , wherein the structured light pattern comprises at least one time and space varying predetermined light pattern.

3. The method of claim 1 , further comprising causing a maneuvering system to maneuver said endoscope in at least two degrees of freedom.

4. The method of claim 1 , wherein said endoscope is an articulating endoscope.

5. The method of claim 1 , comprising, for any point X w in world coordinate system, calculating the coordinate X c of the same point in the camera coordinate system according to the following equation:

X c =C c X w ,

where C c , the camera perspective projection matrix, is of the form

C

c

=

α

[

f

x

kf

y

x

c

0

0

f

y

y

c

0

0

0

1

]

[

R

c

t

c

]

.

where α is a proportion coefficient, fx and fy are the camera focal length scaled to each of the camera image dimensions, k is the shear of the camera coordinate system, c 0 and c 0 are the origin of X c in image coordinates, and R c and t c define the transformation between the world coordinate system and the light source's coordinate system, with R c being a rotation matrix and t c a translation matrix.

6. The method of claim 5 , comprising defining x p 0 to be the x-coordinate of the intersection of the optical axis and the projector.

7. The method of claim 1 , comprising, for any point X w in world coordinate system, calculating the coordinate X p of the same point in the light source coordinate system according to the following equation:

Xp=CpXw,

where Cp, the light source perspective projection matrix, is of the form

C

p

=

α

[

f

p

0

x

p

0

0

0

1

]

[

R

p

t

p

]

where α is a proportion coefficient, f p is the light source focal length scaled to projector dimensions, x p 0 is the origin of X p in projector coordinates, and R p and t p define the transformation between the world coordinate system and the light source's coordinate system, with R p being a rotation matrix and t p a translation matrix.

8. The method of claim 1 , comprising calculating the world coordinates p w of a point P according to the following equation:

(

p

p

p

s

)

-

(

F

c

(

p

w

;

Θ

c

F

p

(

p

w

;

Θ

p

)

=

0

where p p =(x p y p ) t is the pixel coordinate of said point, p x =(x s ) is the stripe value of said point P, F c (P w ; Θ c )=P s −∈ ρ is the noise-free value of the vector of pixel coordinates, where P p is the vector of measured pixel coordinates and ∈ ρ is the vector of errors in the pixel coordinates; F p (P w ; ΘP)=P s −∈ s is the noise-free value of the vector of stripe coordinates, where P s is the vector of measured stripe coordinates and ∈ s is the vector of errors in the stripe coordinates.

9. The method of claim 8 , comprising estimating the world coordinates p w of a point P according to the following non-linear least squares (NLLS) equations:

min

Θ

c



P

p

-

F

c

(

P

w

;

Θ

c



2

⁢

min

Θ

p



P

s

-

F

p

(

P

w

;

Θ

p



2

10. The method of claim 9 , comprising solving said NLLS equations using a NLLS solving algorithm selected from the group consisting of the Gauss-Newton technique, the quasi-Newton technique, and the Levenberg-Marquardt technique.

11. The method of claim 1 , comprising calculating the location, in world coordinates, of a kth point on the object according to the following equation:

p

w

k

=

C

1

,

2

,

1

k

-

x

p

⁢

C

3

,

2

,

1

k

-

y

p

⁢

C

1

,

3

,

1

k

-

x

s

⁢

C

1

,

2

,

2

k

+

x

s

⁢

x

p

⁢

C

3

,

2

,

2

k

+

x

s

⁢

y

p

⁢

C

1

,

3

,

2

k

C

1

,

2

,

1

4

-

x

p

⁢

C

3

,

2

,

1

4

-

y

p

⁢

C

1

,

3

,

1

4

-

x

s

⁢

C

1

,

2

,

2

4

+

x

s

⁢

x

p

⁢

C

3

,

2

,

2

k

+

x

s

⁢

y

p

⁢

C

1

,

3

,

2

4

where C i,j,l k =det(C c i , C c j , C p l , e k ) are constants which depend only on a camera perspective transformation matrix and a projector perspective transformation matrix.

12. The method of claim 1 , further comprising displaying said 3D image on an image display.