Hi, I'm trying to replace incremental BoW with fixed vocabulary trained offline on sub

How can I train an incremental BoW vocabulary offline? about rtabmap HOT 3 OPEN

gvasserm commented on July 4, 2024

How can I train an incremental BoW vocabulary offline?

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matlabbe commented on July 4, 2024

Make sure to launch rtabmap with Kp/DetectorStrategy=2 to use also ORB when you use that dictionary afterwards.

Can you compare with a dictionary created with:

rtabmap-console --Kp/DetectorStrategy 2 --Kp/MaxFeatures 2000 my_dataset_folder

Maybe the high number (2000) of features per image affects the quantization.

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gvasserm commented on July 4, 2024

Make sure to launch rtabmap with Kp/DetectorStrategy=2 to use also ORB when you use that dictionary afterwards.

Can you compare with a dictionary created with:
rtabmap-console --Kp/DetectorStrategy 2 --Kp/MaxFeatures 2000 my_dataset_folder
Maybe the high number (2000) of features per image affects the quantization.

Hi, thank you for reply.
I'm currently using GFTT (--Kp/DetectorStrategy 8) and training the dictionary with saved descriptors from online run.
When I've compared the dictionaries build online (during mapping) and offline (with saved descriptors) it appears that in order to receive the number of loop closures comparable to online trained dictionary you need to remove the duplicate frames from the dataset. This is actually a bit tricky since you need to define dupicate, currently I'm using cosplace solution (global descriptor) for similarity calculation between frames and then clustering the frames by similarity threshold.

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matlabbe commented on July 4, 2024

During online mapping, the similarity is estimated using this function:

rtabmap/corelib/src/Signature.cpp

Lines 250 to 287 in 0f03db9

 float Signature::compareTo(const Signature & s) const 

 { 

 UASSERT(this->sensorData().globalDescriptors().size() == s.sensorData().globalDescriptors().size()); 

 float similarity = 0.0f; 

 int totalDescs = 0; 

 for(size_t i=0; i<this->sensorData().globalDescriptors().size(); ++i) 

 { 

 if(this->sensorData().globalDescriptors()[i].type()==1 && s.sensorData().globalDescriptors()[i].type()==1) 

 { 

 // rescale dot product from -1<->1 to 0<->1 (we assume normalized vectors!) 

 float dotProd = (this->sensorData().globalDescriptors()[i].data().dot(s.sensorData().globalDescriptors()[i].data()) + 1.0f) / 2.0f; 

 UASSERT_MSG(dotProd>=0, "Global descriptors should be normalized!"); 

 similarity += dotProd; 

 totalDescs += 1; 

 } 

 } 

 if(totalDescs) 

 { 

 similarity /= totalDescs; 

 } 

 else 

 { 

 const std::multimap<int, int> & words = s.getWords(); 

 if(!s.isBadSignature() && !this->isBadSignature()) 

 { 

 std::list<std::pair<int, std::pair<int, int> > > pairs; 

 int totalWords = ((int)_words.size()-_invalidWordsCount)>((int)words.size()-s.getInvalidWordsCount())?((int)_words.size()-_invalidWordsCount):((int)words.size()-s.getInvalidWordsCount()); 

 UASSERT(totalWords > 0); 

 EpipolarGeometry::findPairs(words, _words, pairs); 

 similarity = float(pairs.size()) / float(totalWords); 

 } 

 } 

 return similarity;

Then if consecutive images similarity is over Mem/RehearsalSimilarity, it will be discarded. Same thing if the robot is not moving, images are discarded following RGBD/LinearUpdate and RGBD/AngularUpdate parameters.

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How can I train an incremental BoW vocabulary offline? about rtabmap HOT 3 OPEN

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	float Signature::compareTo(const Signature & s) const
	{
	UASSERT(this->sensorData().globalDescriptors().size() == s.sensorData().globalDescriptors().size());

	float similarity = 0.0f;
	int totalDescs = 0;

	for(size_t i=0; i<this->sensorData().globalDescriptors().size(); ++i)
	{
	if(this->sensorData().globalDescriptors()[i].type()==1 && s.sensorData().globalDescriptors()[i].type()==1)
	{
	// rescale dot product from -1<->1 to 0<->1 (we assume normalized vectors!)
	float dotProd = (this->sensorData().globalDescriptors()[i].data().dot(s.sensorData().globalDescriptors()[i].data()) + 1.0f) / 2.0f;
	UASSERT_MSG(dotProd>=0, "Global descriptors should be normalized!");
	similarity += dotProd;
	totalDescs += 1;
	}
	}

	if(totalDescs)
	{
	similarity /= totalDescs;
	}
	else
	{
	const std::multimap<int, int> & words = s.getWords();

	if(!s.isBadSignature() && !this->isBadSignature())
	{
	std::list<std::pair<int, std::pair<int, int> > > pairs;
	int totalWords = ((int)_words.size()-_invalidWordsCount)>((int)words.size()-s.getInvalidWordsCount())?((int)_words.size()-_invalidWordsCount):((int)words.size()-s.getInvalidWordsCount());
	UASSERT(totalWords > 0);
	EpipolarGeometry::findPairs(words, _words, pairs);

	similarity = float(pairs.size()) / float(totalWords);
	}
	}
	return similarity;