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0.15: statistics are now compressed in database. Eigen issue: using linear() instead of rotation() to get rotation from affine3f. Increased loading speed of statistics in DatabaseViewer. Added graph:calcRMSE().

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This commit is contained in:
matlabbe
2017-11-09 12:03:01 -05:00
parent 8cdd138143
commit 71d9816f4b
22 changed files with 685 additions and 602 deletions
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228
tools/KittiDataset/main.cpp
View File

@@ -54,6 +54,7 @@ void showUsage()
" Optional image_2, image_3 and velodyne folders.\n"
" --output Output directory. By default, results are saved in \"path\".\n"
" --gt \"path\" Ground truth path (e.g., ~/KITTI/devkit/cpp/data/odometry/poses/07.txt)\n"
" --quiet Don't show log messages and iteration updates.\n"
" --color Use color images for stereo (image_2 and image_3 folders).\n"
" --disp Generate full disparity.\n"
" --scan Include velodyne scan in node's data.\n"
@@ -107,6 +108,7 @@ int main(int argc, char * argv[])
int scanNormalK = 20;
float scanNormalRadius = 0.0f;
std::string gtPath;
bool quiet = false;
if(argc < 2)
{
showUsage();
@@ -119,6 +121,10 @@ int main(int argc, char * argv[])
{
output = argv[++i];
}
else if(std::strcmp(argv[i], "--quiet") == 0)
{
quiet = true;
}
else if(std::strcmp(argv[i], "--map_update") == 0)
{
mapUpdate = atoi(argv[++i]);
@@ -258,6 +264,7 @@ int main(int argc, char * argv[])
printf(" %s=%s\n", iter->first.c_str(), iter->second.c_str());
}
}
printf("RTAB-Map version: %s\n", RTABMAP_VERSION);
// convert calib.txt to rtabmap format (yaml)
FILE * pFile = 0;
@@ -324,6 +331,12 @@ int main(int argc, char * argv[])
}
printf("Saved calibration \"%s\" to \"%s\"\n", ("rtabmap_calib"+seq).c_str(), output.c_str());
if(quiet)
{
ULogger::setLevel(ULogger::kError);
}
// We use CameraThread only to use postUpdate() method
Transform opticalRotation(0,0,1,0, -1,0,0,color?-0.06:0, 0,-1,0,0);
CameraThread cameraThread(new
@@ -362,6 +375,8 @@ int main(int argc, char * argv[])
{
int totalImages = (int)((CameraStereoImages*)cameraThread.camera())->filenames().size();
printf("Processing %d images...\n", totalImages);
OdometryF2M odom(parameters);
Rtabmap rtabmap;
rtabmap.init(parameters, databasePath);
@@ -395,6 +410,8 @@ int main(int argc, char * argv[])
OdometryInfo odomInfo;
Transform pose = odom.process(data, &odomInfo);
externalStats.insert(std::make_pair("Odometry/LocalBundle/ms", odomInfo.localBundleTime*1000.0f));
externalStats.insert(std::make_pair("Odometry/LocalBundleConstraints/", odomInfo.localBundleConstraints));
externalStats.insert(std::make_pair("Odometry/LocalBundleOutliers/", odomInfo.localBundleOutliers));
externalStats.insert(std::make_pair("Odometry/TotalTime/ms", odomInfo.timeEstimation*1000.0f));
float speed = 0.0f;
if(odomInfo.interval>0.0)
@@ -402,6 +419,11 @@ int main(int argc, char * argv[])
externalStats.insert(std::make_pair("Odometry/Speed/kph", speed));
externalStats.insert(std::make_pair("Odometry/Inliers/", odomInfo.reg.inliers));
externalStats.insert(std::make_pair("Odometry/Features/", odomInfo.features));
externalStats.insert(std::make_pair("Odometry/DistanceTravelled/m", odomInfo.distanceTravelled));
externalStats.insert(std::make_pair("Odometry/KeyFrameAdded/", odomInfo.keyFrameAdded));
externalStats.insert(std::make_pair("Odometry/LocalKeyFrames/", odomInfo.localKeyFrames));
externalStats.insert(std::make_pair("Odometry/LocalMapSize/", odomInfo.localMapSize));
externalStats.insert(std::make_pair("Odometry/LocalScanMapSize/", odomInfo.localScanMapSize));
bool processData = true;
if(iteration % mapUpdate != 0)
@@ -427,16 +449,34 @@ int main(int argc, char * argv[])
rtabmap.process(data, pose, covariance, e.velocity(), externalStats);
covariance = cv::Mat();
}
double slamTime = timer.ticks();
++iteration;
printf("Iteration %d/%d: speed=%dkm/h camera=%dms, odom(quality=%d/%d)=%dms, slam=%dms",
iteration, totalImages, int(speed), int(cameraInfo.timeTotal*1000.0f), odomInfo.reg.inliers, odomInfo.features, int(odomInfo.timeEstimation*1000.0f), int(slamTime*1000.0f));
if(processData && rtabmap.getLoopClosureId()>0)
if(!quiet)
{
printf(" *");
double slamTime = timer.ticks();
float rmse = -1;
if(rtabmap.getStatistics().data().find(Statistics::kGtTranslational_rmse()) != rtabmap.getStatistics().data().end())
{
rmse = rtabmap.getStatistics().data().at(Statistics::kGtTranslational_rmse());
}
++iteration;
if(rmse >= 0.0f)
{
printf("Iteration %d/%d: speed=%dkm/h camera=%dms, odom(quality=%d/%d)=%dms, slam=%dms, rmse=%fm",
iteration, totalImages, int(speed), int(cameraInfo.timeTotal*1000.0f), odomInfo.reg.inliers, odomInfo.features, int(odomInfo.timeEstimation*1000.0f), int(slamTime*1000.0f), rmse);
}
else
{
printf("Iteration %d/%d: speed=%dkm/h camera=%dms, odom(quality=%d/%d)=%dms, slam=%dms",
iteration, totalImages, int(speed), int(cameraInfo.timeTotal*1000.0f), odomInfo.reg.inliers, odomInfo.features, int(odomInfo.timeEstimation*1000.0f), int(slamTime*1000.0f));
}
if(processData && rtabmap.getLoopClosureId()>0)
{
printf(" *");
}
printf("\n");
}
printf("\n");
cameraInfo = CameraInfo();
timer.restart();
@@ -464,15 +504,9 @@ int main(int argc, char * argv[])
if(!gtPath.empty())
{
// Log ground truth statistics (in TUM's RGBD-SLAM format)
// Log ground truth statistics
std::map<int, Transform> groundTruth;
//align with ground truth for more meaningful results
pcl::PointCloud<pcl::PointXYZ> cloud1, cloud2;
cloud1.resize(poses.size());
cloud2.resize(poses.size());
int oi = 0;
int idFirst = 0;
for(std::map<int, Transform>::const_iterator iter=poses.begin(); iter!=poses.end(); ++iter)
{
Transform o, gtPose;
@@ -485,16 +519,10 @@ int main(int argc, char * argv[])
if(!gtPose.isNull())
{
groundTruth.insert(std::make_pair(iter->first, gtPose));
if(oi==0)
{
idFirst = iter->first;
}
cloud1[oi] = pcl::PointXYZ(gtPose.x(), gtPose.y(), gtPose.z());
cloud2[oi++] = pcl::PointXYZ(iter->second.x(), iter->second.y(), iter->second.z());
}
}
// compute KITTI statistics before aligning the poses
// compute KITTI statistics
float t_err = 0.0f;
float r_err = 0.0f;
graph::calcKittiSequenceErrors(uValues(groundTruth), uValues(poses), t_err, r_err);
@@ -502,123 +530,59 @@ int main(int argc, char * argv[])
printf(" KITTI t_err = %f %%\n", t_err);
printf(" KITTI r_err = %f deg/m\n", r_err);
Transform t = Transform::getIdentity();
if(oi>5)
{
cloud1.resize(oi);
cloud2.resize(oi);
t = util3d::transformFromXYZCorrespondencesSVD(cloud2, cloud1);
}
else if(idFirst)
{
t = groundTruth.at(idFirst) * poses.at(idFirst).inverse();
}
if(!t.isIdentity())
{
for(std::map<int, Transform>::iterator iter=poses.begin(); iter!=poses.end(); ++iter)
{
iter->second = t * iter->second;
}
}
std::vector<float> translationalErrors(poses.size());
std::vector<float> rotationalErrors(poses.size());
float sumTranslationalErrors = 0.0f;
float sumRotationalErrors = 0.0f;
float sumSqrdTranslationalErrors = 0.0f;
float sumSqrdRotationalErrors = 0.0f;
float radToDegree = 180.0f / M_PI;
// compute RMSE statistics
float translational_rmse = 0.0f;
float translational_mean = 0.0f;
float translational_median = 0.0f;
float translational_std = 0.0f;
float translational_min = 0.0f;
float translational_max = 0.0f;
float rotational_rmse = 0.0f;
float rotational_mean = 0.0f;
float rotational_median = 0.0f;
float rotational_std = 0.0f;
float rotational_min = 0.0f;
float rotational_max = 0.0f;
oi=0;
for(std::map<int, Transform>::iterator iter=poses.begin(); iter!=poses.end(); ++iter)
graph::calcRMSE(
groundTruth,
poses,
translational_rmse,
translational_mean,
translational_median,
translational_std,
translational_min,
translational_max,
rotational_rmse,
rotational_mean,
rotational_median,
rotational_std,
rotational_min,
rotational_max);
printf(" translational_rmse= %f m\n", translational_rmse);
printf(" rotational_rmse= %f deg\n", rotational_rmse);
pFile = 0;
std::string pathErrors = output+"/rtabmap_rmse"+seq+".txt";
pFile = fopen(pathErrors.c_str(),"w");
if(!pFile)
{
std::map<int, Transform>::const_iterator jter = groundTruth.find(iter->first);
if(jter!=groundTruth.end())
{
Eigen::Vector3f vA = iter->second.toEigen3f().rotation()*Eigen::Vector3f(1,0,0);
Eigen::Vector3f vB = jter->second.toEigen3f().rotation()*Eigen::Vector3f(1,0,0);
double a = pcl::getAngle3D(Eigen::Vector4f(vA[0], vA[1], vA[2], 0), Eigen::Vector4f(vB[0], vB[1], vB[2], 0));
rotationalErrors[oi] = a*radToDegree;
translationalErrors[oi] = iter->second.getDistance(jter->second);
sumTranslationalErrors+=translationalErrors[oi];
sumSqrdTranslationalErrors+=translationalErrors[oi]*translationalErrors[oi];
sumRotationalErrors+=rotationalErrors[oi];
sumSqrdRotationalErrors+=rotationalErrors[oi]*rotationalErrors[oi];
if(oi == 0)
{
translational_min = translational_max = translationalErrors[oi];
rotational_min = rotational_max = rotationalErrors[oi];
}
else
{
if(translationalErrors[oi] < translational_min)
{
translational_min = translationalErrors[oi];
}
else if(translationalErrors[oi] > translational_max)
{
translational_max = translationalErrors[oi];
}
if(rotationalErrors[oi] < rotational_min)
{
rotational_min = rotationalErrors[oi];
}
else if(rotationalErrors[oi] > rotational_max)
{
rotational_max = rotationalErrors[oi];
}
}
++oi;
}
}
translationalErrors.resize(oi);
rotationalErrors.resize(oi);
if(oi)
{
float total = float(oi);
float translational_rmse = std::sqrt(sumSqrdTranslationalErrors/total);
float translational_mean = sumTranslationalErrors/total;
float translational_median = translationalErrors[oi/2];
float translational_std = std::sqrt(uVariance(translationalErrors, translational_mean));
float rotational_rmse = std::sqrt(sumSqrdRotationalErrors/total);
float rotational_mean = sumRotationalErrors/total;
float rotational_median = rotationalErrors[oi/2];
float rotational_std = std::sqrt(uVariance(rotationalErrors, rotational_mean));
printf(" translational_rmse= %f\n", translational_rmse);
printf(" rotational_rmse= %f\n", rotational_rmse);
pFile = 0;
std::string pathErrors = output+"/rtabmap_rmse"+seq+".txt";
pFile = fopen(pathErrors.c_str(),"w");
if(!pFile)
{
UERROR("could not save RMSE results to \"%s\"", pathErrors.c_str());
}
fprintf(pFile, "Ground truth comparison:\n");
fprintf(pFile, " translational_rmse= %f\n", translational_rmse);
fprintf(pFile, " translational_mean= %f\n", translational_mean);
fprintf(pFile, " translational_median= %f\n", translational_median);
fprintf(pFile, " translational_std= %f\n", translational_std);
fprintf(pFile, " translational_min= %f\n", translational_min);
fprintf(pFile, " translational_max= %f\n", translational_max);
fprintf(pFile, " rotational_rmse= %f\n", rotational_rmse);
fprintf(pFile, " rotational_mean= %f\n", rotational_mean);
fprintf(pFile, " rotational_median= %f\n", rotational_median);
fprintf(pFile, " rotational_std= %f\n", rotational_std);
fprintf(pFile, " rotational_min= %f\n", rotational_min);
fprintf(pFile, " rotational_max= %f\n", rotational_max);
fclose(pFile);
UERROR("could not save RMSE results to \"%s\"", pathErrors.c_str());
}
fprintf(pFile, "Ground truth comparison:\n");
fprintf(pFile, " translational_rmse= %f\n", translational_rmse);
fprintf(pFile, " translational_mean= %f\n", translational_mean);
fprintf(pFile, " translational_median= %f\n", translational_median);
fprintf(pFile, " translational_std= %f\n", translational_std);
fprintf(pFile, " translational_min= %f\n", translational_min);
fprintf(pFile, " translational_max= %f\n", translational_max);
fprintf(pFile, " rotational_rmse= %f\n", rotational_rmse);
fprintf(pFile, " rotational_mean= %f\n", rotational_mean);
fprintf(pFile, " rotational_median= %f\n", rotational_median);
fprintf(pFile, " rotational_std= %f\n", rotational_std);
fprintf(pFile, " rotational_min= %f\n", rotational_min);
fprintf(pFile, " rotational_max= %f\n", rotational_max);
fclose(pFile);
}
}
else

226
tools/RgbdDataset/main.cpp
View File

@@ -55,6 +55,7 @@ void showUsage()
" https://gist.github.com/matlabbe/484134a2d9da8ad425362c6669824798). If \n"
" \"groundtruth.txt\" is found in the sequence folder, they will be saved in the database.\n"
" --output Output directory. By default, results are saved in \"path\".\n\n"
" --quiet Don't show log messages and iteration updates.\n"
"%s\n"
"Example:\n\n"
" $ rtabmap-rgbd_dataset \\\n"
@@ -89,6 +90,7 @@ int main(int argc, char * argv[])
ParametersMap parameters;
std::string path;
std::string output;
bool quiet = false;
if(argc < 2)
{
showUsage();
@@ -101,6 +103,10 @@ int main(int argc, char * argv[])
{
output = argv[++i];
}
else if(std::strcmp(argv[i], "--quiet") == 0)
{
quiet = true;
}
}
parameters = Parameters::parseArguments(argc, argv);
path = argv[argc-1];
@@ -126,6 +132,11 @@ int main(int argc, char * argv[])
pathGt.clear();
}
if(quiet)
{
ULogger::setLevel(ULogger::kError);
}
printf("Paths:\n"
" Dataset path: %s\n"
" RGB path: %s\n"
@@ -147,8 +158,9 @@ int main(int argc, char * argv[])
printf(" %s=%s\n", iter->first.c_str(), iter->second.c_str());
}
}
printf("RTAB-Map version: %s\n", RTABMAP_VERSION);
// setup calibraiton file
// setup calibration file
CameraModel model;
std::string sequenceName = UFile(path).getName();
Transform opticalRotation(0,0,1,0, -1,0,0,0, 0,-1,0,0);
@@ -191,6 +203,8 @@ int main(int argc, char * argv[])
{
int totalImages = (int)((CameraRGBDImages*)cameraThread.camera())->filenames().size();
printf("Processing %d images...\n", totalImages);
OdometryF2M odom(parameters);
Rtabmap rtabmap;
rtabmap.init(parameters, databasePath);
@@ -265,16 +279,34 @@ int main(int argc, char * argv[])
rtabmap.process(data, pose, covariance, e.velocity(), externalStats);
covariance = cv::Mat();
}
double slamTime = timer.ticks();
++iteration;
printf("Iteration %d/%d: camera=%dms, odom(quality=%d/%d)=%dms, slam=%dms",
iteration, totalImages, int(cameraInfo.timeTotal*1000.0f), odomInfo.reg.inliers, odomInfo.features, int(odomInfo.timeEstimation*1000.0f), int(slamTime*1000.0f));
if(processData && rtabmap.getLoopClosureId()>0)
if(!quiet)
{
printf(" *");
double slamTime = timer.ticks();
float rmse = -1;
if(rtabmap.getStatistics().data().find(Statistics::kGtTranslational_rmse()) != rtabmap.getStatistics().data().end())
{
rmse = rtabmap.getStatistics().data().at(Statistics::kGtTranslational_rmse());
}
++iteration;
if(rmse >= 0.0f)
{
printf("Iteration %d/%d: camera=%dms, odom(quality=%d/%d)=%dms, slam=%dms, rmse=%fm",
iteration, totalImages, int(cameraInfo.timeTotal*1000.0f), odomInfo.reg.inliers, odomInfo.features, int(odomInfo.timeEstimation*1000.0f), int(slamTime*1000.0f), rmse);
}
else
{
printf("Iteration %d/%d: camera=%dms, odom(quality=%d/%d)=%dms, slam=%dms",
iteration, totalImages, int(cameraInfo.timeTotal*1000.0f), odomInfo.reg.inliers, odomInfo.features, int(odomInfo.timeEstimation*1000.0f), int(slamTime*1000.0f));
}
if(processData && rtabmap.getLoopClosureId()>0)
{
printf(" *");
}
printf("\n");
}
printf("\n");
cameraInfo = CameraInfo();
timer.restart();
@@ -302,15 +334,9 @@ int main(int argc, char * argv[])
if(!pathGt.empty())
{
// Log ground truth statistics (in TUM's RGBD-SLAM format)
// Log ground truth statistics
std::map<int, Transform> groundTruth;
//align with ground truth for more meaningful results
pcl::PointCloud<pcl::PointXYZ> cloud1, cloud2;
cloud1.resize(poses.size());
cloud2.resize(poses.size());
int oi = 0;
int idFirst = 0;
for(std::map<int, Transform>::const_iterator iter=poses.begin(); iter!=poses.end(); ++iter)
{
Transform o, gtPose;
@@ -323,132 +349,70 @@ int main(int argc, char * argv[])
if(!gtPose.isNull())
{
groundTruth.insert(std::make_pair(iter->first, gtPose));
if(oi==0)
{
idFirst = iter->first;
}
cloud1[oi] = pcl::PointXYZ(gtPose.x(), gtPose.y(), gtPose.z());
cloud2[oi++] = pcl::PointXYZ(iter->second.x(), iter->second.y(), iter->second.z());
}
}
Transform t = Transform::getIdentity();
if(oi>5)
{
cloud1.resize(oi);
cloud2.resize(oi);
// compute KITTI statistics
float t_err = 0.0f;
float r_err = 0.0f;
graph::calcKittiSequenceErrors(uValues(groundTruth), uValues(poses), t_err, r_err);
printf("Ground truth comparison:\n");
printf(" KITTI t_err = %f %%\n", t_err);
printf(" KITTI r_err = %f deg/m\n", r_err);
t = util3d::transformFromXYZCorrespondencesSVD(cloud2, cloud1);
}
else if(idFirst)
{
t = groundTruth.at(idFirst) * poses.at(idFirst).inverse();
}
if(!t.isIdentity())
{
for(std::map<int, Transform>::iterator iter=poses.begin(); iter!=poses.end(); ++iter)
{
iter->second = t * iter->second;
}
}
std::vector<float> translationalErrors(poses.size());
std::vector<float> rotationalErrors(poses.size());
float sumTranslationalErrors = 0.0f;
float sumRotationalErrors = 0.0f;
float sumSqrdTranslationalErrors = 0.0f;
float sumSqrdRotationalErrors = 0.0f;
float radToDegree = 180.0f / M_PI;
// compute RMSE statistics
float translational_rmse = 0.0f;
float translational_mean = 0.0f;
float translational_median = 0.0f;
float translational_std = 0.0f;
float translational_min = 0.0f;
float translational_max = 0.0f;
float rotational_rmse = 0.0f;
float rotational_mean = 0.0f;
float rotational_median = 0.0f;
float rotational_std = 0.0f;
float rotational_min = 0.0f;
float rotational_max = 0.0f;
oi=0;
for(std::map<int, Transform>::iterator iter=poses.begin(); iter!=poses.end(); ++iter)
graph::calcRMSE(
groundTruth,
poses,
translational_rmse,
translational_mean,
translational_median,
translational_std,
translational_min,
translational_max,
rotational_rmse,
rotational_mean,
rotational_median,
rotational_std,
rotational_min,
rotational_max);
printf(" translational_rmse= %f m\n", translational_rmse);
printf(" rotational_rmse= %f deg\n", rotational_rmse);
FILE * pFile = 0;
std::string pathErrors = output+"/rtabmap_rmse.txt";
pFile = fopen(pathErrors.c_str(),"w");
if(!pFile)
{
std::map<int, Transform>::const_iterator jter = groundTruth.find(iter->first);
if(jter!=groundTruth.end())
{
Eigen::Vector3f vA = iter->second.toEigen3f().rotation()*Eigen::Vector3f(1,0,0);
Eigen::Vector3f vB = jter->second.toEigen3f().rotation()*Eigen::Vector3f(1,0,0);
double a = pcl::getAngle3D(Eigen::Vector4f(vA[0], vA[1], vA[2], 0), Eigen::Vector4f(vB[0], vB[1], vB[2], 0));
rotationalErrors[oi] = a*radToDegree;
translationalErrors[oi] = iter->second.getDistance(jter->second);
sumTranslationalErrors+=translationalErrors[oi];
sumSqrdTranslationalErrors+=translationalErrors[oi]*translationalErrors[oi];
sumRotationalErrors+=rotationalErrors[oi];
sumSqrdRotationalErrors+=rotationalErrors[oi]*rotationalErrors[oi];
if(oi == 0)
{
translational_min = translational_max = translationalErrors[oi];
rotational_min = rotational_max = rotationalErrors[oi];
}
else
{
if(translationalErrors[oi] < translational_min)
{
translational_min = translationalErrors[oi];
}
else if(translationalErrors[oi] > translational_max)
{
translational_max = translationalErrors[oi];
}
if(rotationalErrors[oi] < rotational_min)
{
rotational_min = rotationalErrors[oi];
}
else if(rotationalErrors[oi] > rotational_max)
{
rotational_max = rotationalErrors[oi];
}
}
++oi;
}
}
translationalErrors.resize(oi);
rotationalErrors.resize(oi);
if(oi)
{
float total = float(oi);
float translational_rmse = std::sqrt(sumSqrdTranslationalErrors/total);
float translational_mean = sumTranslationalErrors/total;
float translational_median = translationalErrors[oi/2];
float translational_std = std::sqrt(uVariance(translationalErrors, translational_mean));
float rotational_rmse = std::sqrt(sumSqrdRotationalErrors/total);
float rotational_mean = sumRotationalErrors/total;
float rotational_median = rotationalErrors[oi/2];
float rotational_std = std::sqrt(uVariance(rotationalErrors, rotational_mean));
printf(" translational_rmse= %f\n", translational_rmse);
printf(" rotational_rmse= %f\n", rotational_rmse);
FILE * pFile = 0;
std::string pathErrors = output+"/rtabmap_rmse.txt";
pFile = fopen(pathErrors.c_str(),"w");
if(!pFile)
{
UERROR("could not save RMSE results to \"%s\"", pathErrors.c_str());
}
fprintf(pFile, "Ground truth comparison:\n");
fprintf(pFile, " translational_rmse= %f\n", translational_rmse);
fprintf(pFile, " translational_mean= %f\n", translational_mean);
fprintf(pFile, " translational_median= %f\n", translational_median);
fprintf(pFile, " translational_std= %f\n", translational_std);
fprintf(pFile, " translational_min= %f\n", translational_min);
fprintf(pFile, " translational_max= %f\n", translational_max);
fprintf(pFile, " rotational_rmse= %f\n", rotational_rmse);
fprintf(pFile, " rotational_mean= %f\n", rotational_mean);
fprintf(pFile, " rotational_median= %f\n", rotational_median);
fprintf(pFile, " rotational_std= %f\n", rotational_std);
fprintf(pFile, " rotational_min= %f\n", rotational_min);
fprintf(pFile, " rotational_max= %f\n", rotational_max);
fclose(pFile);
UERROR("could not save RMSE results to \"%s\"", pathErrors.c_str());
}
fprintf(pFile, "Ground truth comparison:\n");
fprintf(pFile, " translational_rmse= %f\n", translational_rmse);
fprintf(pFile, " translational_mean= %f\n", translational_mean);
fprintf(pFile, " translational_median= %f\n", translational_median);
fprintf(pFile, " translational_std= %f\n", translational_std);
fprintf(pFile, " translational_min= %f\n", translational_min);
fprintf(pFile, " translational_max= %f\n", translational_max);
fprintf(pFile, " rotational_rmse= %f\n", rotational_rmse);
fprintf(pFile, " rotational_mean= %f\n", rotational_mean);
fprintf(pFile, " rotational_median= %f\n", rotational_median);
fprintf(pFile, " rotational_std= %f\n", rotational_std);
fprintf(pFile, " rotational_min= %f\n", rotational_min);
fprintf(pFile, " rotational_max= %f\n", rotational_max);
fclose(pFile);
}
}
else