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Real Time pose estimation of a textured object {#tutorial_real_time_pose}
==============================================
Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields.
The most elemental problem in augmented reality is the estimation of the camera pose respect of an
object in the case of computer vision area to do later some 3D rendering or in the case of robotics
obtain an object pose in order to grasp it and do some manipulation. However, this is not a trivial
problem to solve due to the fact that the most common issue in image processing is the computational
cost of applying a lot of algorithms or mathematical operations for solving a problem which is basic
and immediateley for humans.
Goal
----
In this tutorial is explained how to build a real time application to estimate the camera pose in
order to track a textured object with six degrees of freedom given a 2D image and its 3D textured
model.
The application will have the followings parts:
- Read 3D textured object model and object mesh.
- Take input from Camera or Video.
- Extract ORB features and descriptors from the scene.
- Match scene descriptors with model descriptors using Flann matcher.
- Pose estimation using PnP + Ransac.
- Linear Kalman Filter for bad poses rejection.
Theory
------
In computer vision estimate the camera pose from *n* 3D-to-2D point correspondences is a fundamental
and well understood problem. The most general version of the problem requires estimating the six
degrees of freedom of the pose and five calibration parameters: focal length, principal point,
aspect ratio and skew. It could be established with a minimum of 6 correspondences, using the well
known Direct Linear Transform (DLT) algorithm. There are, though, several simplifications to the
problem which turn into an extensive list of different algorithms that improve the accuracy of the
DLT.
The most common simplification is to assume known calibration parameters which is the so-called
Perspective-*n*-Point problem:
![image](images/pnp.jpg)
**Problem Formulation:** Given a set of correspondences between 3D points \f$p_i\f$ expressed in a world
reference frame, and their 2D projections \f$u_i\f$ onto the image, we seek to retrieve the pose (\f$R\f$
and \f$t\f$) of the camera w.r.t. the world and the focal length \f$f\f$.
OpenCV provides four different approaches to solve the Perspective-*n*-Point problem which return
\f$R\f$ and \f$t\f$. Then, using the following formula it's possible to project 3D points into the image
plane:
\f[s\ \left [ \begin{matrix} u \\ v \\ 1 \end{matrix} \right ] = \left [ \begin{matrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \end{matrix} \right ] \left [ \begin{matrix} r_{11} & r_{12} & r_{13} & t_1 \\ r_{21} & r_{22} & r_{23} & t_2 \\ r_{31} & r_{32} & r_{33} & t_3 \end{matrix} \right ] \left [ \begin{matrix} X \\ Y \\ Z\\ 1 \end{matrix} \right ]\f]
The complete documentation of how to manage with this equations is in @ref cv::Camera Calibration
and 3D Reconstruction .
Source code
-----------
You can find the source code of this tutorial in the
`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/` folder of the OpenCV source library.
The tutorial consists of two main programs:
1. **Model registration**
This applicaton is exclusive to whom don't have a 3D textured model of the object to be detected.
You can use this program to create your own textured 3D model. This program only works for planar
objects, then if you want to model an object with complex shape you should use a sophisticated
software to create it.
The application needs an input image of the object to be registered and its 3D mesh. We have also
to provide the intrinsic parameters of the camera with which the input image was taken. All the
files need to be specified using the absolute path or the relative one from your application’s
working directory. If none files are specified the program will try to open the provided default
parameters.
The application starts up extracting the ORB features and descriptors from the input image and
then uses the mesh along with the [Möller–Trumbore intersection
algorithm](http://http://en.wikipedia.org/wiki/M%C3%B6ller%E2%80%93Trumbore_intersection_algorithm/)
to compute the 3D coordinates of the found features. Finally, the 3D points and the descriptors
are stored in different lists in a file with YAML format which each row is a different point. The
technical background on how to store the files can be found in the @ref fileInputOutputXMLYAML
tutorial.
![image](images/registration.png)
2. **Model detection**
The aim of this application is estimate in real time the object pose given its 3D textured model.
The application starts up loading the 3D textured model in YAML file format with the same
structure explained in the model registration program. From the scene, the ORB features and
descriptors are detected and extracted. Then, is used @ref cv::FlannBasedMatcher with @ref
cv::LshIndexParams to do the matching between the scene descriptors and the model descriptors.
Using the found matches along with @ref cv::solvePnPRansac function the @ref cv::R\` and \f$t\f$ of
the camera are computed. Finally, a KalmanFilter is applied in order to reject bad poses.
In the case that you compiled OpenCV with the samples, you can find it in opencv/build/bin/cpp-tutorial-pnp_detection\`.
Then you can run the application and change some parameters:
@code{.cpp}
This program shows how to detect an object given its 3D textured model. You can choose to use a recorded video or the webcam.
Usage:
./cpp-tutorial-pnp_detection -help
Keys:
'esc' - to quit.
--------------------------------------------------------------------------
Usage: cpp-tutorial-pnp_detection [params]
-c, --confidence (value:0.95)
RANSAC confidence
-e, --error (value:2.0)
RANSAC reprojection errror
-f, --fast (value:true)
use of robust fast match
-h, --help (value:true)
print this message
--in, --inliers (value:30)
minimum inliers for Kalman update
--it, --iterations (value:500)
RANSAC maximum iterations count
-k, --keypoints (value:2000)
number of keypoints to detect
--mesh
path to ply mesh
--method, --pnp (value:0)
PnP method: (0) ITERATIVE - (1) EPNP - (2) P3P - (3) DLS
--model
path to yml model
-r, --ratio (value:0.7)
threshold for ratio test
-v, --video
path to recorded video
@endcode
For example, you can run the application changing the pnp method:
@code{.cpp}
./cpp-tutorial-pnp_detection --method=2
@endcode
Explanation
-----------
Here is explained in detail the code for the real time application:
1. **Read 3D textured object model and object mesh.**
In order to load the textured model I implemented the *class* **Model** which has the function
*load()* that opens a YAML file and take the stored 3D points with its corresponding descriptors.
You can find an example of a 3D textured model in
`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/cookies_ORB.yml`.
@code{.cpp}
/* Load a YAML file using OpenCV */
void Model::load(const std::string path)
{
cv::Mat points3d_mat;
cv::FileStorage storage(path, cv::FileStorage::READ);
storage["points_3d"] >> points3d_mat;
storage["descriptors"] >> descriptors_;
points3d_mat.copyTo(list_points3d_in_);
storage.release();
}
@endcode
In the main program the model is loaded as follows:
@code{.cpp}
Model model; // instantiate Model object
model.load(yml_read_path); // load a 3D textured object model
@endcode
In order to read the model mesh I implemented a *class* **Mesh** which has a function *load()*
that opens a \f$*\f$.ply file and store the 3D points of the object and also the composed triangles.
You can find an example of a model mesh in
`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/box.ply`.
@code{.cpp}
/* Load a CSV with *.ply format */
void Mesh::load(const std::string path)
{
// Create the reader
CsvReader csvReader(path);
// Clear previous data
list_vertex_.clear();
list_triangles_.clear();
// Read from .ply file
csvReader.readPLY(list_vertex_, list_triangles_);
// Update mesh attributes
num_vertexs_ = list_vertex_.size();
num_triangles_ = list_triangles_.size();
}
@endcode
In the main program the mesh is loaded as follows:
@code{.cpp}
Mesh mesh; // instantiate Mesh object
mesh.load(ply_read_path); // load an object mesh
@endcode
You can also load different model and mesh:
@code{.cpp}
./cpp-tutorial-pnp_detection --mesh=/absolute_path_to_your_mesh.ply --model=/absolute_path_to_your_model.yml
@endcode
2. **Take input from Camera or Video**
To detect is necessary capture video. It's done loading a recorded video by passing the absolute
path where it is located in your machine. In order to test the application you can find a recorded
video in `samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/box.mp4`.
@code{.cpp}
cv::VideoCapture cap; // instantiate VideoCapture
cap.open(video_read_path); // open a recorded video
if(!cap.isOpened()) // check if we succeeded
{
std::cout << "Could not open the camera device" << std::endl;
return -1;
}
@endcode
Then the algorithm is computed frame per frame:
@code{.cpp}
cv::Mat frame, frame_vis;
while(cap.read(frame) && cv::waitKey(30) != 27) // capture frame until ESC is pressed
{
frame_vis = frame.clone(); // refresh visualisation frame
// MAIN ALGORITHM
}
@endcode
You can also load different recorded video:
@code{.cpp}
./cpp-tutorial-pnp_detection --video=/absolute_path_to_your_video.mp4
@endcode
3. **Extract ORB features and descriptors from the scene**
The next step is to detect the scene features and extract it descriptors. For this task I
implemented a *class* **RobustMatcher** which has a function for keypoints detection and features
extraction. You can find it in
`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobusMatcher.cpp`. In your
*RobusMatch* object you can use any of the 2D features detectors of OpenCV. In this case I used
@ref cv::ORB features because is based on @ref cv::FAST to detect the keypoints and @ref cv::BRIEF
to extract the descriptors which means that is fast and robust to rotations. You can find more
detailed information about *ORB* in the documentation.
The following code is how to instantiate and set the features detector and the descriptors
extractor:
@code{.cpp}
RobustMatcher rmatcher; // instantiate RobustMatcher
cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints); // instatiate ORB feature detector
cv::DescriptorExtractor * extractor = new cv::OrbDescriptorExtractor(); // instatiate ORB descriptor extractor
rmatcher.setFeatureDetector(detector); // set feature detector
rmatcher.setDescriptorExtractor(extractor); // set descriptor extractor
@endcode
The features and descriptors will be computed by the *RobustMatcher* inside the matching function.
4. **Match scene descriptors with model descriptors using Flann matcher**
It is the first step in our detection algorithm. The main idea is to match the scene descriptors
with our model descriptors in order to know the 3D coordinates of the found features into the
current scene.
Firstly, we have to set which matcher we want to use. In this case is used @ref
cv::FlannBasedMatcher matcher which in terms of computational cost is faster than the @ref
cv::BruteForceMatcher matcher as we increase the trained collectction of features. Then, for
FlannBased matcher the index created is *Multi-Probe LSH: Efficient Indexing for High-Dimensional
Similarity Search* due to *ORB* descriptors are binary.
You can tune the *LSH* and search parameters to improve the matching efficiency:
@code{.cpp}
cv::Ptr<cv::flann::IndexParams> indexParams = cv::makePtr<cv::flann::LshIndexParams>(6, 12, 1); // instantiate LSH index parameters
cv::Ptr<cv::flann::SearchParams> searchParams = cv::makePtr<cv::flann::SearchParams>(50); // instantiate flann search parameters
cv::DescriptorMatcher * matcher = new cv::FlannBasedMatcher(indexParams, searchParams); // instantiate FlannBased matcher
rmatcher.setDescriptorMatcher(matcher); // set matcher
@endcode
Secondly, we have to call the matcher by using *robustMatch()* or *fastRobustMatch()* function.
The difference of using this two functions is its computational cost. The first method is slower
but more robust at filtering good matches because uses two ratio test and a symmetry test. In
contrast, the second method is faster but less robust because only applies a single ratio test to
the matches.
The following code is to get the model 3D points and its descriptors and then call the matcher in
the main program:
@code{.cpp}
// Get the MODEL INFO
std::vector<cv::Point3f> list_points3d_model = model.get_points3d(); // list with model 3D coordinates
cv::Mat descriptors_model = model.get_descriptors(); // list with descriptors of each 3D coordinate
@endcode
@code{.cpp}
// -- Step 1: Robust matching between model descriptors and scene descriptors
std::vector<cv::DMatch> good_matches; // to obtain the model 3D points in the scene
std::vector<cv::KeyPoint> keypoints_scene; // to obtain the 2D points of the scene
if(fast_match)
{
rmatcher.fastRobustMatch(frame, good_matches, keypoints_scene, descriptors_model);
}
else
{
rmatcher.robustMatch(frame, good_matches, keypoints_scene, descriptors_model);
}
@endcode
The following code corresponds to the *robustMatch()* function which belongs to the
*RobustMatcher* class. This function uses the given image to detect the keypoints and extract the
descriptors, match using *two Nearest Neighbour* the extracted descriptors with the given model
descriptors and vice versa. Then, a ratio test is applied to the two direction matches in order to
remove these matches which its distance ratio between the first and second best match is larger
than a given threshold. Finally, a symmetry test is applied in order the remove non symmetrical
matches.
@code{.cpp}
void RobustMatcher::robustMatch( const cv::Mat& frame, std::vector<cv::DMatch>& good_matches,
std::vector<cv::KeyPoint>& keypoints_frame,
const std::vector<cv::KeyPoint>& keypoints_model, const cv::Mat& descriptors_model )
{
// 1a. Detection of the ORB features
this->computeKeyPoints(frame, keypoints_frame);
// 1b. Extraction of the ORB descriptors
cv::Mat descriptors_frame;
this->computeDescriptors(frame, keypoints_frame, descriptors_frame);
// 2. Match the two image descriptors
std::vector<std::vector<cv::DMatch> > matches12, matches21;
// 2a. From image 1 to image 2
matcher_->knnMatch(descriptors_frame, descriptors_model, matches12, 2); // return 2 nearest neighbours
// 2b. From image 2 to image 1
matcher_->knnMatch(descriptors_model, descriptors_frame, matches21, 2); // return 2 nearest neighbours
// 3. Remove matches for which NN ratio is > than threshold
// clean image 1 -> image 2 matches
int removed1 = ratioTest(matches12);
// clean image 2 -> image 1 matches
int removed2 = ratioTest(matches21);
// 4. Remove non-symmetrical matches
symmetryTest(matches12, matches21, good_matches);
}
@endcode
After the matches filtering we have to subtract the 2D and 3D correspondences from the found scene
keypoints and our 3D model using the obtained *DMatches* vector. For more information about @ref
cv::DMatch check the documentation.
@code{.cpp}
// -- Step 2: Find out the 2D/3D correspondences
std::vector<cv::Point3f> list_points3d_model_match; // container for the model 3D coordinates found in the scene
std::vector<cv::Point2f> list_points2d_scene_match; // container for the model 2D coordinates found in the scene
for(unsigned int match_index = 0; match_index < good_matches.size(); ++match_index)
{
cv::Point3f point3d_model = list_points3d_model[ good_matches[match_index].trainIdx ]; // 3D point from model
cv::Point2f point2d_scene = keypoints_scene[ good_matches[match_index].queryIdx ].pt; // 2D point from the scene
list_points3d_model_match.push_back(point3d_model); // add 3D point
list_points2d_scene_match.push_back(point2d_scene); // add 2D point
}
@endcode
You can also change the ratio test threshold, the number of keypoints to detect as well as use or
not the robust matcher:
@code{.cpp}
./cpp-tutorial-pnp_detection --ratio=0.8 --keypoints=1000 --fast=false
@endcode
5. **Pose estimation using PnP + Ransac**
Once with the 2D and 3D correspondences we have to apply a PnP algorithm in order to estimate the
camera pose. The reason why we have to use @ref cv::solvePnPRansac instead of @ref cv::solvePnP is
due to the fact that after the matching not all the found correspondences are correct and, as like
as not, there are false correspondences or also called *outliers*. The [Random Sample
Consensus](http://en.wikipedia.org/wiki/RANSAC) or *Ransac* is a non-deterministic iterative
method which estimate parameters of a mathematical model from observed data producing an
aproximate result as the number of iterations increase. After appyling *Ransac* all the *outliers*
will be eliminated to then estimate the camera pose with a certain probability to obtain a good
solution.
For the camera pose estimation I have implemented a *class* **PnPProblem**. This *class* has 4
atributes: a given calibration matrix, the rotation matrix, the translation matrix and the
rotation-translation matrix. The intrinsic calibration parameters of the camera which you are
using to estimate the pose are necessary. In order to obtain the parameters you can check @ref
CameraCalibrationSquareChessBoardTutorial and @ref cameraCalibrationOpenCV tutorials.
The following code is how to declare the *PnPProblem class* in the main program:
@code{.cpp}
// Intrinsic camera parameters: UVC WEBCAM
double f = 55; // focal length in mm
double sx = 22.3, sy = 14.9; // sensor size
double width = 640, height = 480; // image size
double params_WEBCAM[] = { width*f/sx, // fx
height*f/sy, // fy
width/2, // cx
height/2}; // cy
PnPProblem pnp_detection(params_WEBCAM); // instantiate PnPProblem class
@endcode
The following code is how the *PnPProblem class* initialises its atributes:
@code{.cpp}
// Custom constructor given the intrinsic camera parameters
PnPProblem::PnPProblem(const double params[])
{
_A_matrix = cv::Mat::zeros(3, 3, CV_64FC1); // intrinsic camera parameters
_A_matrix.at<double>(0, 0) = params[0]; // [ fx 0 cx ]
_A_matrix.at<double>(1, 1) = params[1]; // [ 0 fy cy ]
_A_matrix.at<double>(0, 2) = params[2]; // [ 0 0 1 ]
_A_matrix.at<double>(1, 2) = params[3];
_A_matrix.at<double>(2, 2) = 1;
_R_matrix = cv::Mat::zeros(3, 3, CV_64FC1); // rotation matrix
_t_matrix = cv::Mat::zeros(3, 1, CV_64FC1); // translation matrix
_P_matrix = cv::Mat::zeros(3, 4, CV_64FC1); // rotation-translation matrix
}
@endcode
OpenCV provides four PnP methods: ITERATIVE, EPNP, P3P and DLS. Depending on the application type,
the estimation method will be different. In the case that we want to make a real time application,
the more suitable methods are EPNP and P3P due to that are faster than ITERATIVE and DLS at
finding an optimal solution. However, EPNP and P3P are not especially robust in front of planar
surfaces and sometimes the pose estimation seems to have a mirror effect. Therefore, in this this
tutorial is used ITERATIVE method due to the object to be detected has planar surfaces.
The OpenCV Ransac implementation wants you to provide three parameters: the maximum number of
iterations until stop the algorithm, the maximum allowed distance between the observed and
computed point projections to consider it an inlier and the confidence to obtain a good result.
You can tune these paramaters in order to improve your algorithm performance. Increasing the
number of iterations you will have a more accurate solution, but will take more time to find a
solution. Increasing the reprojection error will reduce the computation time, but your solution
will be unaccurate. Decreasing the confidence your arlgorithm will be faster, but the obtained
solution will be unaccurate.
The following parameters work for this application:
@code{.cpp}
// RANSAC parameters
int iterationsCount = 500; // number of Ransac iterations.
float reprojectionError = 2.0; // maximum allowed distance to consider it an inlier.
float confidence = 0.95; // ransac successful confidence.
@endcode
The following code corresponds to the *estimatePoseRANSAC()* function which belongs to the
*PnPProblem class*. This function estimates the rotation and translation matrix given a set of
2D/3D correspondences, the desired PnP method to use, the output inliers container and the Ransac
parameters:
@code{.cpp}
// Estimate the pose given a list of 2D/3D correspondences with RANSAC and the method to use
void PnPProblem::estimatePoseRANSAC( const std::vector<cv::Point3f> &list_points3d, // list with model 3D coordinates
const std::vector<cv::Point2f> &list_points2d, // list with scene 2D coordinates
int flags, cv::Mat &inliers, int iterationsCount, // PnP method; inliers container
float reprojectionError, float confidence ) // Ransac parameters
{
cv::Mat distCoeffs = cv::Mat::zeros(4, 1, CV_64FC1); // vector of distortion coefficients
cv::Mat rvec = cv::Mat::zeros(3, 1, CV_64FC1); // output rotation vector
cv::Mat tvec = cv::Mat::zeros(3, 1, CV_64FC1); // output translation vector
bool useExtrinsicGuess = false; // if true the function uses the provided rvec and tvec values as
// initial approximations of the rotation and translation vectors
cv::solvePnPRansac( list_points3d, list_points2d, _A_matrix, distCoeffs, rvec, tvec,
useExtrinsicGuess, iterationsCount, reprojectionError, confidence,
inliers, flags );
Rodrigues(rvec,_R_matrix); // converts Rotation Vector to Matrix
_t_matrix = tvec; // set translation matrix
this->set_P_matrix(_R_matrix, _t_matrix); // set rotation-translation matrix
}
@endcode
In the following code are the 3th and 4th steps of the main algorithm. The first, calling the
above function and the second taking the output inliers vector from Ransac to get the 2D scene
points for drawing purpose. As seen in the code we must be sure to apply Ransac if we have
matches, in the other case, the function @ref cv::solvePnPRansac crashes due to any OpenCV *bug*.
@code{.cpp}
if(good_matches.size() > 0) // None matches, then RANSAC crashes
{
// -- Step 3: Estimate the pose using RANSAC approach
pnp_detection.estimatePoseRANSAC( list_points3d_model_match, list_points2d_scene_match,
pnpMethod, inliers_idx, iterationsCount, reprojectionError, confidence );
// -- Step 4: Catch the inliers keypoints to draw
for(int inliers_index = 0; inliers_index < inliers_idx.rows; ++inliers_index)
{
int n = inliers_idx.at<int>(inliers_index); // i-inlier
cv::Point2f point2d = list_points2d_scene_match[n]; // i-inlier point 2D
list_points2d_inliers.push_back(point2d); // add i-inlier to list
}
@endcode
Finally, once the camera pose has been estimated we can use the \f$R\f$ and \f$t\f$ in order to compute
the 2D projection onto the image of a given 3D point expressed in a world reference frame using
the showed formula on *Theory*.
The following code corresponds to the *backproject3DPoint()* function which belongs to the
*PnPProblem class*. The function backproject a given 3D point expressed in a world reference frame
onto a 2D image:
@code{.cpp}
// Backproject a 3D point to 2D using the estimated pose parameters
cv::Point2f PnPProblem::backproject3DPoint(const cv::Point3f &point3d)
{
// 3D point vector [x y z 1]'
cv::Mat point3d_vec = cv::Mat(4, 1, CV_64FC1);
point3d_vec.at<double>(0) = point3d.x;
point3d_vec.at<double>(1) = point3d.y;
point3d_vec.at<double>(2) = point3d.z;
point3d_vec.at<double>(3) = 1;
// 2D point vector [u v 1]'
cv::Mat point2d_vec = cv::Mat(4, 1, CV_64FC1);
point2d_vec = _A_matrix * _P_matrix * point3d_vec;
// Normalization of [u v]'
cv::Point2f point2d;
point2d.x = point2d_vec.at<double>(0) / point2d_vec.at<double>(2);
point2d.y = point2d_vec.at<double>(1) / point2d_vec.at<double>(2);
return point2d;
}
@endcode
The above function is used to compute all the 3D points of the object *Mesh* to show the pose of
the object.
You can also change RANSAC parameters and PnP method:
@code{.cpp}
./cpp-tutorial-pnp_detection --error=0.25 --confidence=0.90 --iterations=250 --method=3
@endcode
6. **Linear Kalman Filter for bad poses rejection**
Is it common in computer vision or robotics fields that after applying detection or tracking
techniques, bad results are obtained due to some sensor errors. In order to avoid these bad
detections in this tutorial is explained how to implement a Linear Kalman Filter. The Kalman
Filter will be applied after detected a given number of inliers.
You can find more information about what [Kalman
Filter](http://en.wikipedia.org/wiki/Kalman_filter) is. In this tutorial it's used the OpenCV
implementation of the @ref cv::Kalman Filter based on [Linear Kalman Filter for position and
orientation tracking](http://campar.in.tum.de/Chair/KalmanFilter) to set the dynamics and
measurement models.
Firstly, we have to define our state vector which will have 18 states: the positional data (x,y,z)
with its first and second derivatives (velocity and acceleration), then rotation is added in form
of three euler angles (roll, pitch, jaw) together with their first and second derivatives (angular
velocity and acceleration)
\f[X = (x,y,z,\dot x,\dot y,\dot z,\ddot x,\ddot y,\ddot z,\psi,\theta,\phi,\dot \psi,\dot \theta,\dot \phi,\ddot \psi,\ddot \theta,\ddot \phi)^T\f]
Secondly, we have to define the number of measuremnts which will be 6: from \f$R\f$ and \f$t\f$ we can
extract \f$(x,y,z)\f$ and \f$(\psi,\theta,\phi)\f$. In addition, we have to define the number of control
actions to apply to the system which in this case will be *zero*. Finally, we have to define the
differential time between measurements which in this case is \f$1/T\f$, where *T* is the frame rate of
the video.
@code{.cpp}
cv::KalmanFilter KF; // instantiate Kalman Filter
int nStates = 18; // the number of states
int nMeasurements = 6; // the number of measured states
int nInputs = 0; // the number of action control
double dt = 0.125; // time between measurements (1/FPS)
initKalmanFilter(KF, nStates, nMeasurements, nInputs, dt); // init function
@endcode
The following code corresponds to the *Kalman Filter* initialisation. Firstly, is set the process
noise, the measurement noise and the error covariance matrix. Secondly, are set the transition
matrix which is the dynamic model and finally the measurement matrix, which is the measurement
model.
You can tune the process and measurement noise to improve the *Kalman Filter* performance. As the
measurement noise is reduced the faster will converge doing the algorithm sensitive in front of
bad measurements.
@code{.cpp}
void initKalmanFilter(cv::KalmanFilter &KF, int nStates, int nMeasurements, int nInputs, double dt)
{
KF.init(nStates, nMeasurements, nInputs, CV_64F); // init Kalman Filter
cv::setIdentity(KF.processNoiseCov, cv::Scalar::all(1e-5)); // set process noise
cv::setIdentity(KF.measurementNoiseCov, cv::Scalar::all(1e-4)); // set measurement noise
cv::setIdentity(KF.errorCovPost, cv::Scalar::all(1)); // error covariance
/* DYNAMIC MODEL */
// [1 0 0 dt 0 0 dt2 0 0 0 0 0 0 0 0 0 0 0]
// [0 1 0 0 dt 0 0 dt2 0 0 0 0 0 0 0 0 0 0]
// [0 0 1 0 0 dt 0 0 dt2 0 0 0 0 0 0 0 0 0]
// [0 0 0 1 0 0 dt 0 0 0 0 0 0 0 0 0 0 0]
// [0 0 0 0 1 0 0 dt 0 0 0 0 0 0 0 0 0 0]
// [0 0 0 0 0 1 0 0 dt 0 0 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 0 0 0 1 0 0 dt 0 0 dt2 0 0]
// [0 0 0 0 0 0 0 0 0 0 1 0 0 dt 0 0 dt2 0]
// [0 0 0 0 0 0 0 0 0 0 0 1 0 0 dt 0 0 dt2]
// [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 dt 0 0]
// [0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 dt 0]
// [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 dt]
// [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
// [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0]
// [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]
// position
KF.transitionMatrix.at<double>(0,3) = dt;
KF.transitionMatrix.at<double>(1,4) = dt;
KF.transitionMatrix.at<double>(2,5) = dt;
KF.transitionMatrix.at<double>(3,6) = dt;
KF.transitionMatrix.at<double>(4,7) = dt;
KF.transitionMatrix.at<double>(5,8) = dt;
KF.transitionMatrix.at<double>(0,6) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(1,7) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(2,8) = 0.5*pow(dt,2);
// orientation
KF.transitionMatrix.at<double>(9,12) = dt;
KF.transitionMatrix.at<double>(10,13) = dt;
KF.transitionMatrix.at<double>(11,14) = dt;
KF.transitionMatrix.at<double>(12,15) = dt;
KF.transitionMatrix.at<double>(13,16) = dt;
KF.transitionMatrix.at<double>(14,17) = dt;
KF.transitionMatrix.at<double>(9,15) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(10,16) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(11,17) = 0.5*pow(dt,2);
/* MEASUREMENT MODEL */
// [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
// [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
// [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
KF.measurementMatrix.at<double>(0,0) = 1; // x
KF.measurementMatrix.at<double>(1,1) = 1; // y
KF.measurementMatrix.at<double>(2,2) = 1; // z
KF.measurementMatrix.at<double>(3,9) = 1; // roll
KF.measurementMatrix.at<double>(4,10) = 1; // pitch
KF.measurementMatrix.at<double>(5,11) = 1; // yaw
}
@endcode
In the following code is the 5th step of the main algorithm. When the obtained number of inliers
after *Ransac* is over the threshold, the measurements matrix is filled and then the *Kalman
Filter* is updated:
@code{.cpp}
// -- Step 5: Kalman Filter
// GOOD MEASUREMENT
if( inliers_idx.rows >= minInliersKalman )
{
// Get the measured translation
cv::Mat translation_measured(3, 1, CV_64F);
translation_measured = pnp_detection.get_t_matrix();
// Get the measured rotation
cv::Mat rotation_measured(3, 3, CV_64F);
rotation_measured = pnp_detection.get_R_matrix();
// fill the measurements vector
fillMeasurements(measurements, translation_measured, rotation_measured);
}
// Instantiate estimated translation and rotation
cv::Mat translation_estimated(3, 1, CV_64F);
cv::Mat rotation_estimated(3, 3, CV_64F);
// update the Kalman filter with good measurements
updateKalmanFilter( KF, measurements,
translation_estimated, rotation_estimated);
@endcode
The following code corresponds to the *fillMeasurements()* function which converts the measured
[Rotation Matrix to Eulers
angles](http://euclideanspace.com/maths/geometry/rotations/conversions/matrixToEuler/index.htm)
and fill the measurements matrix along with the measured translation vector:
@code{.cpp}
void fillMeasurements( cv::Mat &measurements,
const cv::Mat &translation_measured, const cv::Mat &rotation_measured)
{
// Convert rotation matrix to euler angles
cv::Mat measured_eulers(3, 1, CV_64F);
measured_eulers = rot2euler(rotation_measured);
// Set measurement to predict
measurements.at<double>(0) = translation_measured.at<double>(0); // x
measurements.at<double>(1) = translation_measured.at<double>(1); // y
measurements.at<double>(2) = translation_measured.at<double>(2); // z
measurements.at<double>(3) = measured_eulers.at<double>(0); // roll
measurements.at<double>(4) = measured_eulers.at<double>(1); // pitch
measurements.at<double>(5) = measured_eulers.at<double>(2); // yaw
}
@endcode
The following code corresponds to the *updateKalmanFilter()* function which update the Kalman
Filter and set the estimated Rotation Matrix and translation vector. The estimated Rotation Matrix
comes from the estimated [Euler angles to Rotation
Matrix](http://euclideanspace.com/maths/geometry/rotations/conversions/eulerToMatrix/index.htm).
@code{.cpp}
void updateKalmanFilter( cv::KalmanFilter &KF, cv::Mat &measurement,
cv::Mat &translation_estimated, cv::Mat &rotation_estimated )
{
// First predict, to update the internal statePre variable
cv::Mat prediction = KF.predict();
// The "correct" phase that is going to use the predicted value and our measurement
cv::Mat estimated = KF.correct(measurement);
// Estimated translation
translation_estimated.at<double>(0) = estimated.at<double>(0);
translation_estimated.at<double>(1) = estimated.at<double>(1);
translation_estimated.at<double>(2) = estimated.at<double>(2);
// Estimated euler angles
cv::Mat eulers_estimated(3, 1, CV_64F);
eulers_estimated.at<double>(0) = estimated.at<double>(9);
eulers_estimated.at<double>(1) = estimated.at<double>(10);
eulers_estimated.at<double>(2) = estimated.at<double>(11);
// Convert estimated quaternion to rotation matrix
rotation_estimated = euler2rot(eulers_estimated);
}
@endcode
The 6th step is set the estimated rotation-translation matrix:
@code{.cpp}
// -- Step 6: Set estimated projection matrix
pnp_detection_est.set_P_matrix(rotation_estimated, translation_estimated);
@endcode
The last and optional step is draw the found pose. To do it I implemented a function to draw all
the mesh 3D points and an extra reference axis:
@code{.cpp}
// -- Step X: Draw pose
drawObjectMesh(frame_vis, &mesh, &pnp_detection, green); // draw current pose
drawObjectMesh(frame_vis, &mesh, &pnp_detection_est, yellow); // draw estimated pose
double l = 5;
std::vector<cv::Point2f> pose_points2d;
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(0,0,0))); // axis center
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(l,0,0))); // axis x
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(0,l,0))); // axis y
pose_points2d.push_back(pnp_detection_est.backproject3DPoint(cv::Point3f(0,0,l))); // axis z
draw3DCoordinateAxes(frame_vis, pose_points2d); // draw axes
@endcode
You can also modify the minimum inliers to update Kalman Filter:
@code{.cpp}
./cpp-tutorial-pnp_detection --inliers=20
@endcode
Results
-------
The following videos are the results of pose estimation in real time using the explained detection
algorithm using the following parameters:
@code{.cpp}
// Robust Matcher parameters
int numKeyPoints = 2000; // number of detected keypoints
float ratio = 0.70f; // ratio test
bool fast_match = true; // fastRobustMatch() or robustMatch()
// RANSAC parameters
int iterationsCount = 500; // number of Ransac iterations.
int reprojectionError = 2.0; // maximum allowed distance to consider it an inlier.
float confidence = 0.95; // ransac successful confidence.
// Kalman Filter parameters
int minInliersKalman = 30; // Kalman threshold updating
@endcode
You can watch the real time pose estimation on the [YouTube
here](http://www.youtube.com/user/opencvdev/videos).
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