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Added parameter "Mem/UseOdometryFeatures=false" to use directly features already extracted from odometry for the vocabulary (issue #32)

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matlabbe
2016-02-22 17:57:40 -05:00
parent ccc4b4a6c2
commit d7ef170d33
15 changed files with 175 additions and 73 deletions
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corelib/src/Memory.cpp
+33 -2
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@@ -90,6 +90,7 @@ Memory::Memory(const ParametersMap & parameters) :
_rehearsalMaxAngle(Parameters::defaultRGBDAngularUpdate()),
_rehearsalWeightIgnoredWhileMoving(Parameters::defaultMemRehearsalWeightIgnoredWhileMoving()),
_useDepthAsMask(Parameters::defaultMemUseDepthAsMask()),
_useOdometryFeatures(Parameters::defaultMemUseOdomFeatures()),
_idCount(kIdStart),
_idMapCount(kIdStart),
_lastSignature(0),
@@ -404,6 +405,7 @@ void Memory::parseParameters(const ParametersMap & parameters)
Parameters::parse(parameters, Parameters::kRGBDAngularUpdate(), _rehearsalMaxAngle);
Parameters::parse(parameters, Parameters::kMemRehearsalWeightIgnoredWhileMoving(), _rehearsalWeightIgnoredWhileMoving);
Parameters::parse(parameters, Parameters::kMemUseDepthAsMask(), _useDepthAsMask);
Parameters::parse(parameters, Parameters::kMemUseOdomFeatures(), _useOdometryFeatures);
UASSERT_MSG(_maxStMemSize >= 0, uFormat("value=%d", _maxStMemSize).c_str());
UASSERT_MSG(_similarityThreshold >= 0.0f && _similarityThreshold <= 1.0f, uFormat("value=%f", _similarityThreshold).c_str());
@@ -3102,10 +3104,11 @@ Signature * Memory::createSignature(const SensorData & data, const Transform & p
}
std::vector<cv::Point3f> keypoints3D;
if(data.keypoints().size() == 0)
if(!_useOdometryFeatures || data.keypoints().size() == 0)
{
if(_feature2D->getMaxFeatures() >= 0 && !data.imageRaw().empty() && !isIntermediateNode)
{
UINFO("Extract features");
cv::Mat imageMono;
if(data.imageRaw().channels() == 3)
{
@@ -3166,10 +3169,38 @@ Signature * Memory::createSignature(const SensorData & data, const Transform & p
}
else if(!isIntermediateNode)
{
UINFO("Use odometry features");
keypoints = data.keypoints();
descriptors = data.descriptors().clone();
keypoints3D = _feature2D->generateKeypoints3D(data, keypoints);
UASSERT(descriptors.empty() || descriptors.rows == (int)keypoints.size());
if(keypoints.size() > _feature2D->getMaxFeatures())
{
_feature2D->limitKeypoints(keypoints, descriptors, _feature2D->getMaxFeatures());
}
if(descriptors.empty())
{
descriptors = _feature2D->generateDescriptors(data.imageRaw(), keypoints);
t = timer.ticks();
if(stats) stats->addStatistic(Statistics::kTimingMemDescriptors_extraction(), t*1000.0f);
UDEBUG("time descriptors (%d) = %fs", descriptors.rows, t);
}
UDEBUG("ratio=%f, meanWordsPerLocation=%d", _badSignRatio, meanWordsPerLocation);
if(descriptors.rows && descriptors.rows < _badSignRatio * float(meanWordsPerLocation))
{
descriptors = cv::Mat();
}
else if((!data.depthRaw().empty() && data.cameraModels().size() && data.cameraModels()[0].isValidForProjection()) ||
(!data.rightRaw().empty() && data.stereoCameraModel().isValidForProjection()))
{
keypoints3D = _feature2D->generateKeypoints3D(data, keypoints);
t = timer.ticks();
if(stats) stats->addStatistic(Statistics::kTimingMemKeypoints_3D(), t*1000.0f);
UDEBUG("time keypoints 3D (%d) = %fs", (int)keypoints3D.size(), t);
}
}
if(_parallelized)