Added CameraInfo class (added Camera tab in Statistics panel). Scans/userData are not saved anymore when Mem/BinDataKept=false. StereoACameraImages: Fixed error when scan path is not set. Refactoring of OdometryOpticalFlow class to provide variance when 3D->2D estimation is used.

This commit is contained in:
matlabbe
2015-11-30 18:49:28 -05:00
parent d9c9db934a
commit e58f907440
18 changed files with 267 additions and 144 deletions

View File

@@ -31,6 +31,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <opencv2/highgui/highgui.hpp>
#include "rtabmap/core/SensorData.h"
#include "rtabmap/core/CameraInfo.h"
#include <set>
#include <stack>
#include <list>
@@ -50,12 +51,11 @@ class RTABMAP_EXP Camera
{
public:
virtual ~Camera();
SensorData takeImage();
SensorData takeImage(CameraInfo * info = 0);
virtual bool init(const std::string & calibrationFolder = ".", const std::string & cameraName = "") = 0;
virtual bool isCalibrated() const = 0;
virtual std::string getSerial() const = 0;
int getNextSeqID() {return ++_seq;}
//getters
float getImageRate() const {return _imageRate;}
@@ -78,6 +78,8 @@ protected:
*/
virtual SensorData captureImage() = 0;
int getNextSeqID() {return ++_seq;}
private:
float _imageRate;
Transform _localTransform;

View File

@@ -29,6 +29,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <rtabmap/utilite/UEvent.h>
#include "rtabmap/core/SensorData.h"
#include "rtabmap/core/CameraInfo.h"
namespace rtabmap
{
@@ -43,11 +44,11 @@ public:
};
public:
CameraEvent(const cv::Mat & image, int seq=0, double stamp = 0.0, const std::string & cameraName = "") :
CameraEvent(const cv::Mat & image, int seq=0, double stamp = 0.0, const std::string & cameraName = std::string()) :
UEvent(kCodeData),
data_(image, seq, stamp),
cameraName_(cameraName)
data_(image, seq, stamp)
{
cameraInfo_.cameraName_ = cameraName;
}
CameraEvent() :
@@ -55,23 +56,36 @@ public:
{
}
CameraEvent(const SensorData & data, const std::string & cameraName = "") :
CameraEvent(const SensorData & data) :
UEvent(kCodeData),
data_(data)
{
}
CameraEvent(const SensorData & data, const std::string & cameraName) :
UEvent(kCodeData),
data_(data)
{
cameraInfo_.cameraName_ = cameraName;
}
CameraEvent(const SensorData & data, const CameraInfo & cameraInfo) :
UEvent(kCodeData),
data_(data),
cameraName_(cameraName)
cameraInfo_(cameraInfo)
{
}
// Image or descriptors
const SensorData & data() const {return data_;}
const std::string & cameraName() const {return cameraName_;}
const std::string & cameraName() const {return cameraInfo_.cameraName_;}
const CameraInfo & info() const {return cameraInfo_;}
virtual ~CameraEvent() {}
virtual std::string getClassName() const {return std::string("CameraEvent");}
private:
SensorData data_;
std::string cameraName_;
CameraInfo cameraInfo_;
};
} // namespace rtabmap

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@@ -0,0 +1,56 @@
/*
Copyright (c) 2010-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the Universite de Sherbrooke nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#pragma once
#include <string>
namespace rtabmap
{
class CameraInfo
{
public:
CameraInfo() :
cameraName_(""),
id_(0),
timeCapture_(0.0),
timeDisparity_(0.0),
timeMirroring_(0.0)
{
}
virtual ~CameraInfo() {}
std::string cameraName_;
int id_;
float timeCapture_;
float timeDisparity_;
float timeMirroring_;
};
} // namespace rtabmap

View File

@@ -64,6 +64,7 @@ public:
double getPnPReprojError() const {return _pnpReprojError;}
int getPnPFlags() const {return _pnpFlags;}
const Transform & previousTransform() const {return previousTransform_;}
bool isVarianceFromInliersCount() const {return _varianceFromInliersCount;}
private:
virtual Transform computeTransform(const SensorData & image, OdometryInfo * info = 0) = 0;

View File

@@ -51,7 +51,7 @@ Transform RTABMAP_EXP estimateMotion3DTo2D(
int flagsPnP = 0,
const Transform & guess = Transform::getIdentity(),
const std::map<int, pcl::PointXYZ> & words3B = std::map<int, pcl::PointXYZ>(),
double * varianceOut = 0,
double * varianceOut = 0, // mean reproj error if words3B is not set
std::vector<int> * matchesOut = 0,
std::vector<int> * inliersOut = 0);

View File

@@ -61,7 +61,7 @@ Camera::~Camera()
}
}
SensorData Camera::takeImage()
SensorData Camera::takeImage(CameraInfo * info)
{
bool warnFrameRateTooHigh = false;
float actualFrameRate = 0;
@@ -91,14 +91,20 @@ SensorData Camera::takeImage()
UTimer timer;
SensorData data = this->captureImage();
double captureTime = timer.ticks();
if(warnFrameRateTooHigh)
{
UWARN("Camera: Cannot reach target image rate %f Hz, current rate is %f Hz and capture time = %f s.",
_imageRate, actualFrameRate, timer.ticks());
_imageRate, actualFrameRate, captureTime);
}
else
{
UDEBUG("Time capturing image = %fs", timer.ticks());
UDEBUG("Time capturing image = %fs", captureTime);
}
if(info)
{
info->id_ = data.id();
info->timeCapture_ = captureTime;
}
return data;
}

View File

@@ -66,7 +66,8 @@ CameraImages::CameraImages(const std::string & path,
_count(0),
_dir(0),
_countScan(0),
_scanDir(0)
_scanDir(0),
_scanMaxPts(0)
{
}
@@ -152,6 +153,7 @@ bool CameraImages::init(const std::string & calibrationFolder, const std::string
}
if(!_scanPath.empty())
{
UINFO("scan path=%s", _scanPath.c_str());
_scanDir = new UDirectory(_scanPath, "pcd bin"); // "bin" is for KITTI format
if(_scanPath[_scanPath.size()-1] != '\\' && _scanPath[_scanPath.size()-1] != '/')
{

View File

@@ -67,9 +67,9 @@ void CameraThread::setImageRate(float imageRate)
void CameraThread::mainLoop()
{
UTimer timer;
UDEBUG("");
SensorData data = _camera->takeImage();
CameraInfo info;
SensorData data = _camera->takeImage(&info);
if(!data.imageRaw().empty())
{
@@ -79,6 +79,7 @@ void CameraThread::mainLoop()
}
if(_mirroring && data.cameraModels().size() == 1)
{
UTimer timer;
cv::Mat tmpRgb;
cv::flip(data.imageRaw(), tmpRgb, 1);
data.setImageRaw(tmpRgb);
@@ -98,9 +99,11 @@ void CameraThread::mainLoop()
cv::flip(data.depthRaw(), tmpDepth, 1);
data.setDepthOrRightRaw(tmpDepth);
}
info.timeMirroring_ = timer.ticks();
}
if(_stereoToDepth && data.stereoCameraModel().isValid() && !data.rightRaw().empty())
{
UTimer timer;
cv::Mat depth = util2d::depthFromDisparity(
util2d::disparityFromStereoImages(data.imageRaw(), data.rightRaw()),
data.stereoCameraModel().left().fx(),
@@ -108,9 +111,11 @@ void CameraThread::mainLoop()
data.setCameraModel(data.stereoCameraModel().left());
data.setDepthOrRightRaw(depth);
data.setStereoCameraModel(StereoCameraModel());
info.timeDisparity_ = timer.ticks();
UINFO("Computing disparity = %f s", info.timeDisparity_);
}
this->post(new CameraEvent(data, _camera->getSerial()));
info.cameraName_ = _camera->getSerial();
this->post(new CameraEvent(data, info));
}
else if(!this->isKilled())
{

View File

@@ -3548,13 +3548,6 @@ Signature * Memory::createSignature(const SensorData & data, const Transform & p
}
else
{
rtabmap::CompressionThread ctDepth2d(laserScan);
rtabmap::CompressionThread ctUserData(data.userDataRaw());
ctDepth2d.start();
ctUserData.start();
ctDepth2d.join();
ctUserData.join();
s = new Signature(id,
_idMapCount,
isIntermediateNode?-1:0, // tag intermediate nodes as weight=-1
@@ -3563,25 +3556,23 @@ Signature * Memory::createSignature(const SensorData & data, const Transform & p
pose,
stereoCameraModel.isValid()?
SensorData(
ctDepth2d.getCompressedData(),
maxLaserScanMaxPts,
data.laserScanMaxRange(),
cv::Mat(),
0,
0,
cv::Mat(),
cv::Mat(),
stereoCameraModel,
id,
0,
ctUserData.getCompressedData()):
0):
SensorData(
ctDepth2d.getCompressedData(),
maxLaserScanMaxPts,
data.laserScanMaxRange(),
cv::Mat(),
0,
0,
cv::Mat(),
cv::Mat(),
cameraModels,
id,
0,
ctUserData.getCompressedData()));
0));
}
s->setWords(words);
s->setWords3(words3D);

View File

@@ -62,6 +62,7 @@ Odometry::Odometry(const rtabmap::ParametersMap & parameters) :
{
Parameters::parse(parameters, Parameters::kOdomResetCountdown(), _resetCountdown);
Parameters::parse(parameters, Parameters::kVisMinInliers(), _minInliers);
UASSERT(_minInliers >= 1);
Parameters::parse(parameters, Parameters::kVisInlierDistance(), _inlierDistance);
Parameters::parse(parameters, Parameters::kVisIterations(), _iterations);
Parameters::parse(parameters, Parameters::kVisRefineIterations(), _refineIterations);

View File

@@ -65,6 +65,7 @@ OdometryBOW::OdometryBOW(const ParametersMap & parameters) :
customParameters.insert(ParametersPair(Parameters::kMemBinDataKept(), "false"));
customParameters.insert(ParametersPair(Parameters::kMemSTMSize(), "0"));
customParameters.insert(ParametersPair(Parameters::kMemNotLinkedNodesKept(), "false"));
customParameters.insert(ParametersPair(Parameters::kMemSaveDepth16Format(), "false"));
int nn = Parameters::defaultVisNNType();
float nndr = Parameters::defaultVisNNDR();
int featureType = Parameters::defaultVisFeatureType();
@@ -120,7 +121,6 @@ OdometryBOW::OdometryBOW(const ParametersMap & parameters) :
// init the local map with a all 3D features contained in the database
customParameters.insert(ParametersPair(Parameters::kMemIncrementalMemory(), "false"));
customParameters.insert(ParametersPair(Parameters::kMemInitWMWithAllNodes(), "true"));
customParameters.insert(ParametersPair(Parameters::kMemSaveDepth16Format(), "false"));
_memory = new Memory(customParameters);
if(!_memory->init(_fixedLocalMapPath, false, ParametersMap()))
{
@@ -257,7 +257,7 @@ Transform OdometryBOW::computeTransform(
this->getPnPFlags(),
this->getPose(),
uMultimapToMap(newSignature->getWords3()),
&variance,
isVarianceFromInliersCount()?0:&variance, // don't compute variance if we use inliers
&matches,
&inliers);
}

View File

@@ -218,7 +218,9 @@ Transform OdometryOpticalFlow::computeTransform(
int ki = 0;
for(unsigned int i=0; i<status.size(); ++i)
{
if(status[i])
if(status[i] &&
uIsInBounds(newCorners[i].x, 0.0f, float(data.depthOrRightRaw().cols)) &&
uIsInBounds(newCorners[i].y, 0.0f, float(data.depthOrRightRaw().rows)))
{
refCorners3DKept->at(ki) = refCorners3D_->at(i);
objectPointsKept[ki] = objectPoints[i];
@@ -232,71 +234,123 @@ Transform OdometryOpticalFlow::computeTransform(
refCornersKept.resize(ki);
newCornersKept.resize(ki);
if(ki && ki >= this->getMinInliers())
correspondences = ki;
if(correspondences && correspondences >= this->getMinInliers())
{
// get new 3D points
pcl::PointCloud<pcl::PointXYZ>::Ptr newCorners3DKept;
if(!isVarianceFromInliersCount() || this->getEstimationType() != 1)
{
// Don't compute the new 3D points if the variance is not required on PnP estimation
if(!data.rightRaw().empty())
{
// stereo
newCorners3DKept = util3d::generateKeypoints3DStereo(
newCornersKept,
newLeftFrame,
data.rightRaw(),
data.stereoCameraModel().left().fx(),
data.stereoCameraModel().baseline(),
data.stereoCameraModel().left().cx(),
data.stereoCameraModel().left().cy(),
data.stereoCameraModel().left().localTransform(),
stereoWinSize_,
stereoMaxLevel_,
stereoIterations_,
stereoEps_,
stereoMaxSlope_);
}
else
{
//depth
std::vector<cv::KeyPoint> newCornersKeptKpt;
cv::KeyPoint::convert(newCornersKept, newCornersKeptKpt);
newCorners3DKept = util3d::generateKeypoints3DDepth(
newCornersKeptKpt,
data.depthRaw(),
data.cameraModels());
}
UASSERT(newCorners3DKept.get() != 0);
}
std::vector<int> inliersV;
if(this->getEstimationType() == 1) // PnP
{
// find correspondences
if(this->isInfoDataFilled() && info)
{
info->refCorners = refCornersKept;
info->newCorners = newCornersKept;
}
correspondences = refCornersKept.size();
if(correspondences >= this->getMinInliers())
{
//PnPRansac
std::vector<int> inliersV;
cv::solvePnPRansac(
objectPointsKept,
newCornersKept,
K,
cv::Mat(),
rvec,
tvec,
true,
this->getIterations(),
this->getPnPReprojError(),
//PnPRansac
cv::solvePnPRansac(
objectPointsKept,
newCornersKept,
K,
cv::Mat(),
rvec,
tvec,
true,
this->getIterations(),
this->getPnPReprojError(),
#if CV_MAJOR_VERSION < 3
0, // min inliers
0, // min inliers
#else
0.99, // confidence
0.99, // confidence
#endif
inliersV,
this->getPnPFlags());
inliersV,
this->getPnPFlags());
cv::Rodrigues(rvec, R);
Transform pnp(R.at<double>(0,0), R.at<double>(0,1), R.at<double>(0,2), tvec.at<double>(0),
R.at<double>(1,0), R.at<double>(1,1), R.at<double>(1,2), tvec.at<double>(1),
R.at<double>(2,0), R.at<double>(2,1), R.at<double>(2,2), tvec.at<double>(2));
cv::Rodrigues(rvec, R);
Transform pnp(R.at<double>(0,0), R.at<double>(0,1), R.at<double>(0,2), tvec.at<double>(0),
R.at<double>(1,0), R.at<double>(1,1), R.at<double>(1,2), tvec.at<double>(1),
R.at<double>(2,0), R.at<double>(2,1), R.at<double>(2,2), tvec.at<double>(2));
inliers = (int)inliersV.size();
if((int)inliersV.size() >= this->getMinInliers())
{
// make it incremental
output = (localTransform * pnp).inverse();
variance = 1; // FIXME, is there a way to compute a variance from the PNP approach?
}
else
{
UWARN("PnP not enough inliers (%d < %d), rejecting the transform...", (int)inliersV.size(), this->getMinInliers());
}
inliers = (int)inliersV.size();
if((int)inliersV.size() >= this->getMinInliers())
{
// make it incremental
output = (localTransform * pnp).inverse();
if(this->isInfoDataFilled() && info)
// compute variance from 3D correspondences error
variance = 1;
if(!isVarianceFromInliersCount())
{
info->cornerInliers = inliersV;
UASSERT(objectPointsKept.size() == newCorners3DKept->size());
std::vector<float> errorSqrdDists(inliersV.size());
int oi = 0;
for(unsigned int i=0; i<inliersV.size(); ++i)
{
if(pcl::isFinite(newCorners3DKept->at(inliersV[i])))
{
const cv::Point3f & objPt = objectPointsKept[inliersV[i]];
pcl::PointXYZ newPt = util3d::transformPoint(newCorners3DKept->at(inliersV[i]), output);
errorSqrdDists[oi++] = uNormSquared(objPt.x-newPt.x, objPt.y-newPt.y, objPt.z-newPt.z);
}
}
errorSqrdDists.resize(oi);
if(errorSqrdDists.size())
{
std::sort(errorSqrdDists.begin(), errorSqrdDists.end());
double median_error_sqr = (double)errorSqrdDists[errorSqrdDists.size () >> 1];
variance = 2.1981 * median_error_sqr;
}
}
}
else
{
UWARN("Not enough correspondences (%d < %d)", correspondences, this->getMinInliers());
UWARN("PnP not enough inliers (%d < %d), rejecting the transform...", (int)inliersV.size(), this->getMinInliers());
}
if(this->isInfoDataFilled() && info)
{
info->cornerInliers = inliersV;
}
}
else
{
// Get 3D correspondences
// Get 3D correspondences (remove NaN)
pcl::PointCloud<pcl::PointXYZ>::Ptr correspondencesRef(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr correspondencesNew(new pcl::PointCloud<pcl::PointXYZ>);
correspondencesRef->resize(newCornersKept.size());
@@ -307,66 +361,22 @@ Transform OdometryOpticalFlow::computeTransform(
info->newCorners.resize(newCornersKept.size());
}
int oi = 0;
if(!data.rightRaw().empty())
UASSERT(newCorners3DKept->size() == newCornersKept.size());
for(unsigned int i=0; i<newCornersKept.size(); ++i)
{
// stereo
pcl::PointCloud<pcl::PointXYZ>::Ptr newCorners3D = util3d::generateKeypoints3DStereo(
newCornersKept,
newLeftFrame,
data.rightRaw(),
data.stereoCameraModel().left().fx(),
data.stereoCameraModel().baseline(),
data.stereoCameraModel().left().cx(),
data.stereoCameraModel().left().cy(),
Transform::getIdentity(),
stereoWinSize_,
stereoMaxLevel_,
stereoIterations_,
stereoEps_,
stereoMaxSlope_);
UASSERT(newCorners3D->size() == refCorners3DKept->size());
for(unsigned int i=0; i<newCorners3D->size(); ++i)
if(pcl::isFinite(newCorners3DKept->at(i)) &&
(this->getMaxDepth() == 0.0f || newCorners3DKept->at(i).z < this->getMaxDepth()))
{
if(pcl::isFinite(newCorners3D->at(i)) && (this->getMaxDepth() <= 0.0f || newCorners3D->at(i).z < this->getMaxDepth()))
{
//Add 3D correspondences!
correspondencesRef->at(oi) = refCorners3DKept->at(i);
correspondencesNew->at(oi) = util3d::transformPoint(newCorners3D->at(i), localTransform);
if(this->isInfoDataFilled() && info)
{
info->refCorners[oi] = refCornersKept[i];
info->newCorners[oi] = newCornersKept[i];
}
++oi;
}
}// end loop
}
else
{
//depth
for(unsigned int i=0; i<newCornersKept.size(); ++i)
{
if(uIsInBounds(newCornersKept[i].x, 0.0f, float(data.depthRaw().cols)) &&
uIsInBounds(newCornersKept[i].y, 0.0f, float(data.depthRaw().rows)))
{
pcl::PointXYZ pt = util3d::projectDepthTo3D(data.depthRaw(), newCornersKept[i].x, newCorners[i].y,
data.cameraModels()[0].cx(), data.cameraModels()[0].cy(), data.cameraModels()[0].fx(), data.cameraModels()[0].fy(), true);
if(pcl::isFinite(pt) &&
(this->getMaxDepth() == 0.0f || pt.z < this->getMaxDepth()))
{
//Add 3D correspondences!
correspondencesRef->at(oi) = refCorners3DKept->at(i);
correspondencesNew->at(oi) = util3d::transformPoint(pt, localTransform);
//Add 3D correspondences!
correspondencesRef->at(oi) = refCorners3DKept->at(i);
correspondencesNew->at(oi) = newCorners3DKept->at(i);
if(this->isInfoDataFilled() && info)
{
info->refCorners[oi] = refCornersKept[i];
info->newCorners[oi] = newCornersKept[i];
}
++oi;
}
if(this->isInfoDataFilled() && info)
{
info->refCorners[oi] = refCornersKept[i];
info->newCorners[oi] = newCornersKept[i];
}
++oi;
}
}
correspondencesRef->resize(oi);
@@ -381,7 +391,6 @@ Transform OdometryOpticalFlow::computeTransform(
if(correspondences >= this->getMinInliers())
{
std::vector<int> inliersV;
UTimer timerRANSAC;
Transform t = util3d::transformFromXYZCorrespondences(
correspondencesNew,
@@ -402,17 +411,17 @@ Transform OdometryOpticalFlow::computeTransform(
{
UWARN("Transform not valid (inliers = %d/%d)", inliers, correspondences);
}
if(this->isInfoDataFilled() && info)
{
info->cornerInliers = inliersV;
}
}
else
{
UWARN("Not enough correspondences (%d)", correspondences);
}
}
if(this->isInfoDataFilled() && info)
{
info->cornerInliers = inliersV;
}
}
}
else
@@ -458,6 +467,7 @@ Transform OdometryOpticalFlow::computeTransform(
newCorners3D->resize(newCorners.size());
std::vector<cv::Point2f> newCornersFiltered(newCorners.size());
int oi=0;
UTimer corner3dTimer;
if(!data.rightRaw().empty())
{
/// stereo
@@ -515,6 +525,7 @@ Transform OdometryOpticalFlow::computeTransform(
}
}
}
UDEBUG("Computing 3d corners = %f s", corner3dTimer.ticks());
newCornersFiltered.resize(oi);
newCorners3D->resize(oi);

View File

@@ -320,7 +320,7 @@ Transform RegistrationVis::computeTransformation(
_PnPFlags,
Transform::getIdentity(),
uMultimapToMap(*words3To),
&variance,
_varianceFromInliersCount?0:&variance,
0,
&inliersV);
inliersCount = (int)inliersV.size();

View File

@@ -34,6 +34,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "rtabmap/core/EpipolarGeometry.h"
#include <rtabmap/utilite/ULogger.h>
#include <rtabmap/utilite/UConversion.h>
#include <rtabmap/utilite/UMath.h>
#include <opencv2/video/tracking.hpp>
@@ -71,7 +72,9 @@ pcl::PointCloud<pcl::PointXYZ>::Ptr generateKeypoints3DDepth(
for(unsigned int i=0; i!=keypoints.size(); ++i)
{
int cameraIndex = int(keypoints[i].pt.x / subImageWidth);
UASSERT(cameraIndex < (int)cameraModels.size());
UASSERT_MSG(cameraIndex < (int)cameraModels.size(),
uFormat("cameraIndex=%d, models=%d, kpt.x=%f, subImageWidth=%f",
cameraIndex, (int)cameraModels.size(), keypoints[i].pt.x, subImageWidth).c_str());
pcl::PointXYZ pt = util3d::projectDepthTo3D(
depth,
keypoints[i].pt.x-subImageWidth*cameraIndex,

View File

@@ -159,6 +159,18 @@ Transform estimateMotion3DTo2D(
*varianceOut = 2.1981 * median_error_sqr;
}
}
else if(varianceOut)
{
// compute variance, which is the rms of reprojection errors
std::vector<cv::Point2f> imagePointsReproj;
cv::projectPoints(objectPoints, rvec, tvec, K, cv::Mat(), imagePointsReproj);
float err = 0.0f;
for(unsigned int i=0; i<inliers.size(); ++i)
{
err += uNormSquared(imagePoints.at(inliers[i]).x - imagePointsReproj.at(inliers[i]).x, imagePoints.at(inliers[i]).y - imagePointsReproj.at(inliers[i]).y);
}
*varianceOut = std::sqrt(err/float(inliers.size()));
}
}
}