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Parameters renamed: "OdomLocalMap" group is now "OdomF2M" for Frame to Map odometry. Visual registration feature matching: using guess transform to limit the radius of correspondences "Vis/CorGuessWinSize=16"

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This commit is contained in:
matlabbe
2016-02-22 16:13:14 -05:00
parent 0d68f74d80
commit ccc4b4a6c2
22 changed files with 844 additions and 638 deletions
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corelib/src/VWDictionary.cpp
+39 -12
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@@ -61,7 +61,8 @@ public:
nextIndex_(0),
featuresType_(0),
featuresDim_(0),
isLSH_(false)
isLSH_(false),
useDistanceL1_(false)
{
}
virtual ~FlannIndex()
@@ -1099,8 +1100,6 @@ std::vector<int> VWDictionary::findNN(const std::list<VisualWord *> & vws) const
{
UTimer timer;
timer.start();
std::vector<int> resultIds(vws.size(), 0);
unsigned int k=2; // k nearest neighbor
if(_visualWords.size() && vws.size())
{
@@ -1110,20 +1109,15 @@ std::vector<int> VWDictionary::findNN(const std::list<VisualWord *> & vws) const
if(dim != (*vws.begin())->getDescriptor().cols)
{
UERROR("Descriptors (size=%d) are not the same size as already added words in dictionary(size=%d)", (*vws.begin())->getDescriptor().cols, dim);
return resultIds;
return std::vector<int>(vws.size(), 0);
}
if(type != (*vws.begin())->getDescriptor().type())
{
UERROR("Descriptors (type=%d) are not the same type as already added words in dictionary(type=%d)", (*vws.begin())->getDescriptor().type(), type);
return resultIds;
return std::vector<int>(vws.size(), 0);
}
std::vector<std::vector<cv::DMatch> > matches;
bool bruteForce = false;
cv::Mat results;
cv::Mat dists;
// fill the request matrix
int index = 0;
VisualWord * vw;
@@ -1139,10 +1133,43 @@ std::vector<int> VWDictionary::findNN(const std::list<VisualWord *> & vws) const
}
ULOGGER_DEBUG("Preparation time = %fs", timer.ticks());
return findNN(query);
}
return std::vector<int>(vws.size(), 0);
}
std::vector<int> VWDictionary::findNN(const cv::Mat & query) const
{
UTimer timer;
timer.start();
std::vector<int> resultIds(query.rows, 0);
unsigned int k=2; // k nearest neighbor
if(_visualWords.size() && query.rows)
{
int dim = _visualWords.begin()->second->getDescriptor().cols;
int type = _visualWords.begin()->second->getDescriptor().type();
if(dim != query.cols)
{
UERROR("Descriptors (size=%d) are not the same size as already added words in dictionary(size=%d)", query.cols, dim);
return resultIds;
}
if(type != query.type())
{
UERROR("Descriptors (type=%d) are not the same type as already added words in dictionary(type=%d)", query.type(), type);
return resultIds;
}
std::vector<std::vector<cv::DMatch> > matches;
bool bruteForce = false;
cv::Mat results;
cv::Mat dists;
if(_flannIndex->isBuilt() || (!_dataTree.empty() && _dataTree.rows >= (int)k))
{
//Find nearest neighbors
UDEBUG("newPts.total()=%d ", query.total());
UDEBUG("query.rows=%d ", query.rows);
if(_strategy == kNNFlannNaive || _strategy == kNNFlannKdTree || _strategy == kNNFlannLSH)
{
@@ -1231,7 +1258,7 @@ std::vector<int> VWDictionary::findNN(const std::list<VisualWord *> & vws) const
}
ULOGGER_DEBUG("Search not yet indexed words time = %fs", timer.ticks());
for(unsigned int i=0; i<vws.size(); ++i)
for(unsigned int i=0; i<query.rows; ++i)
{
std::multimap<float, int> fullResults; // Contains results from the kd-tree search [and the naive search in new words]
if(!bruteForce && dists.cols)