Utility class that allows for 6D pose estimation from a set of at least 4 coplanar points. More...

#include <CoplanarPointPoseEstimator.h>

Inheritance diagram for icl::geom::CoplanarPointPoseEstimator:

Public Types
enum	ReferenceFrame { cameraFrame, worldFrame }
	Reference frame enumeration. More...
enum	PoseEstimationAlgorithm { HomographyBasedOnly, SimplexSampling, SamplingCoarse, SamplingMedium, SamplingFine, SamplingCustom }
	Algorithm, that is used for pose-estimation. More...
Public Member Functions
	CoplanarPointPoseEstimator (ReferenceFrame returnedPosesReferenceFrame=worldFrame, PoseEstimationAlgorithm a=SimplexSampling)
	Default constructor with given reference-frame for the returned poses.
	~CoplanarPointPoseEstimator ()
	Destructor.
	CoplanarPointPoseEstimator (const CoplanarPointPoseEstimator &other)
	Copy constructor.
CoplanarPointPoseEstimator &	operator= (const CoplanarPointPoseEstimator &other)
	Assignment operator.
ReferenceFrame	getReferenceFrame () const
	returns the current reference frame value
void	setReferenceFrame (ReferenceFrame f)
	sets the refernce-frame property
math::FixedMatrix< float, 4, 4 >	getPose (int n, const utils::Point32f modelPoints, const utils::Point32f imagePoints, const Camera &cam)
	main working function that estimates the pose from given model- and image points
Private Member Functions
void	propertyChangedCallback (const Property &p)
	Internally used to sync property settings with internal data.
void	robustPoseCorrection (int n, const utils::Point32f *modelPoints, std::vector< utils::Point32f > &ips)
	Internally used to correct the first transformation matrix using robust pose estimation algorithm.
Private Attributes
Data *	data
	Internally used data pointer.

Detailed Description

Utility class that allows for 6D pose estimation from a set of at least 4 coplanar points.

General Information

In contrast to the POSIT algorithms (see icl::Posit), this class is able to compute the 6D-pose of an object from coplanar points.

Pose Detection Problem

Given at least 4 model-points (which are 2D because of the fact, that they have to be coplanar), where no subset of 3 points is collinear, and a set of corresponding pixel-locations of these points, the pose-estimation problem can be redefined as homography problem.

The Object Coordinate Frame

As all reference points of the object are coplanar, you can define the objects coordinate in that way that all reference points lie within the z=0 plane. As the algorithm uses this as prior knowledge, the CoplanarPointPoseEstimator::getPose interface does allow to pass 2D object reference points only.

Algorithms

We implemented the algorithm from the paper "Pose estimation based on four coplanar point correspondences" written by Yang et. al. and published 2009. Internally, a 9 by 2N matrix A is created. The solution for the pose detection problem is basically achieved by minimizing |Ax| using SVD. Finally, the resulting x can be used to obtain the transformation matrix. We also added one heuristic to the method described in the paper above. As the projection of a plane is ambiguous (you can project from the front and from the back), we always use always use the solution, where the resulting z-value is in front of the camera (Otherwise, we would not be able to see it). Mathematically, this means, that we do not always use x, but sometimes also -x, which obviously leads to the same error |Ax|.

Reference Frames

CoplanarPointPoseEstimator instances can be set up to return the estimated pose w.r.t. the world frame or with respect to the camera frame. If you obtain the pose w.r.t. the camera frame, you have to multiply it with the inverted camera coordinate system transformation matrix.

Integration with ICL's Scene class

In order to use the CoplanarPointPoseEstimator to estimate an objects pose matrix for visualization using an instance of the icl::Scene class, you can simply define an objects base vertices in the object coordinate frame:
e.g. (1,1,1,1), (1, 1,-1,1), ... for a unity cube. For moving these points to their real location in the 3D scene, you simply have to transform these vectors by the obtained 'w.r.t. world frame' transformation matrix.

Benchmarks

Simple Pose estimation with 4 points needs about 80 ns on an Intel(R) Xeon(R) E5530 (2.40GHz). If 9 points are used, it needs about 110 ns.

Member Enumeration Documentation

enum icl::geom::CoplanarPointPoseEstimator::PoseEstimationAlgorithm

Algorithm, that is used for pose-estimation.

TODO: describe the algorithms

Enumerator:

HomographyBasedOnly	uses the above described algorithm (Algorithms) only
SimplexSampling	performs simplex sampling for optimization (very fast and very accurate!
SamplingCoarse	use some predefined sampling parameters for brute force coase sampling (fast)
SamplingMedium	use some predefined sampling parameters for brute force medium sampling (average speed)
SamplingFine	use some predefined sampling parameters for brute force fine sampling (slow)
SamplingCustom	uses custom properties to define sampling parameters for brute force sampling

enum icl::geom::CoplanarPointPoseEstimator::ReferenceFrame

Reference frame enumeration.

Enumerator:

cameraFrame	poses are returned w.r.t. the camera frame
worldFrame	poses are returned w.r.t. the world frame

Constructor & Destructor Documentation

icl::geom::CoplanarPointPoseEstimator::CoplanarPointPoseEstimator	(	ReferenceFrame	returnedPosesReferenceFrame = `worldFrame`,
		PoseEstimationAlgorithm	a = `SimplexSampling`
	)

Default constructor with given reference-frame for the returned poses.

Please note that the Downhill Simplex based pose optimization is very accurate and very fast. Using other modes does usually slowdown the pose estimation process and also decrease the result quality. Hovever the brute force search is still provided due to 'historic' reasons

icl::geom::CoplanarPointPoseEstimator::~CoplanarPointPoseEstimator ( )

Destructor.

icl::geom::CoplanarPointPoseEstimator::CoplanarPointPoseEstimator ( const CoplanarPointPoseEstimator & other )

Copy constructor.

Member Function Documentation

math::FixedMatrix<float,4,4> icl::geom::CoplanarPointPoseEstimator::getPose	(	int	n,
		const utils::Point32f *	modelPoints,
		const utils::Point32f *	imagePoints,
		const Camera &	cam
	)

main working function that estimates the pose from given model- and image points

See also:: Pose Detection Problem; The Object Coordinate Frame; Algorithms

note: the camera class is only forward-declared for this file

ReferenceFrame icl::geom::CoplanarPointPoseEstimator::getReferenceFrame ( ) const

returns the current reference frame value

CoplanarPointPoseEstimator& icl::geom::CoplanarPointPoseEstimator::operator= ( const CoplanarPointPoseEstimator & other )

Assignment operator.

void icl::geom::CoplanarPointPoseEstimator::propertyChangedCallback ( const Property & p ) [private]

Internally used to sync property settings with internal data.

void icl::geom::CoplanarPointPoseEstimator::robustPoseCorrection	(	int	n,
		const utils::Point32f *	modelPoints,
		std::vector< utils::Point32f > &	ips
	)		`[private]`

Internally used to correct the first transformation matrix using robust pose estimation algorithm.

void icl::geom::CoplanarPointPoseEstimator::setReferenceFrame ( ReferenceFrame f )

sets the refernce-frame property

Member Data Documentation

Data* icl::geom::CoplanarPointPoseEstimator::data [private]

Internally used data pointer.

The documentation for this class was generated from the following file:

/home/jenkins/workspace/icl-manual-trunk/icl-manual/ICLGeom/src/ICLGeom/CoplanarPointPoseEstimator.h

Public Types

Public Member Functions

Private Member Functions

Private Attributes