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关键帧数据是通过预先训练好的词典，维护一个vector的链表，每个链表对应一个vocabulary的id，对应存储含有这个vocabulary的关键帧链表。

#ifndef KEYFRAMEDATABASE_H #define KEYFRAMEDATABASE_H #include <vector> #include <list> #include <set> #include "KeyFrame.h" #include "Frame.h" #include "ORBVocabulary.h" #include<mutex> namespace ORB_SLAM2 { class KeyFrame; class Frame; class KeyFrameDatabase { public: KeyFrameDatabase(const ORBVocabulary &voc); void add(KeyFrame* pKF); void erase(KeyFrame* pKF); void clear(); // Loop Detection std::vector<KeyFrame *> DetectLoopCandidates(KeyFrame* pKF, float minScore); // Relocalization std::vector<KeyFrame*> DetectRelocalizationCandidates(Frame* F); protected: // Associated vocabulary const ORBVocabulary* mpVoc; // Inverted file std::vector<list<KeyFrame*> > mvInvertedFile; // Mutex std::mutex mMutex; }; } //namespace ORB_SLAM #endif

#include "KeyFrameDatabase.h" #include "KeyFrame.h" #include "Thirdparty/DBoW2/DBoW2/BowVector.h" #include<mutex> using namespace std; namespace ORB_SLAM2 { //构造函数，传入的是ORBVocabulary //关键帧数据库通过预先训练好的词典，维护一个向量 mvInvertedFile KeyFrameDatabase::KeyFrameDatabase (const ORBVocabulary &voc): mpVoc(&voc) { mvInvertedFile.resize(voc.size());// } //根据词袋模型增加关键帧 void KeyFrameDatabase::add(KeyFrame *pKF) { unique_lock<mutex> lock(mMutex); //mvInvertedFile[i]表示第i个WordId的所有关键帧, vit->first取出的就是WordID for(DBoW2::BowVector::const_iterator vit= pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit!=vend; vit++) mvInvertedFile[vit->first].push_back(pKF); } //删除对应的关键帧 void KeyFrameDatabase::erase(KeyFrame* pKF) { unique_lock<mutex> lock(mMutex); // Erase elements in the Inverse File for the entry //对整个vector进行遍历 for(DBoW2::BowVector::const_iterator vit=pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit!=vend; vit++) { // List of keyframes that share the word //对每个voc进行再次遍历 list<KeyFrame*> &lKFs = mvInvertedFile[vit->first]; //如果list中存在指定KF,删除后重新检索其他list for(list<KeyFrame*>::iterator lit=lKFs.begin(), lend= lKFs.end(); lit!=lend; lit++) { if(pKF==*lit) { lKFs.erase(lit); break; } } } } //清除对应的mvInvertedFile,重新初始化mvInvertedFile的尺寸 void KeyFrameDatabase::clear() { mvInvertedFile.clear(); mvInvertedFile.resize(mpVoc->size()); } //闭环检测 //需要参考关键帧去寻找闭环候选帧 vector<KeyFrame*> KeyFrameDatabase::DetectLoopCandidates(KeyFrame* pKF, float minScore) { set<KeyFrame*> spConnectedKeyFrames = pKF->GetConnectedKeyFrames(); list<KeyFrame*> lKFsSharingWords; // Search all keyframes that share a word with current keyframes // Discard keyframes connected to the query keyframe { unique_lock<mutex> lock(mMutex); //找出与当前关键帧pKF有公共单词的所有关键帧pKFi,不包括与当前帧相连的关键帧 for(DBoW2::BowVector::const_iterator vit=pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit != vend; vit++) { list<KeyFrame*> &lKFs = mvInvertedFile[vit->first]; for(list<KeyFrame*>::iterator lit=lKFs.begin(), lend= lKFs.end(); lit!=lend; lit++) { KeyFrame* pKFi=*lit; if(pKFi->mnLoopQuery!=pKF->mnId) { pKFi->mnLoopWords=0; if(!spConnectedKeyFrames.count(pKFi)) { pKFi->mnLoopQuery=pKF->mnId; lKFsSharingWords.push_back(pKFi); } } pKFi->mnLoopWords++; } } } if(lKFsSharingWords.empty()) return vector<KeyFrame*>(); list<pair<float,KeyFrame*> > lScoreAndMatch; // Only compare against those keyframes that share enough words // 统计所有闭环候选帧中与pKF具有共同单词最多的单词数， int maxCommonWords=0; for(list<KeyFrame*>::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) { if((*lit)->mnLoopWords>maxCommonWords) maxCommonWords=(*lit)->mnLoopWords; } //设定一个阈值，只有当共有单词数大于0.8*maxCommonWords以及匹配得分大于给定的minScore的关键帧，存入IScoreAndMatch int minCommonWords = maxCommonWords*0.8f; int nscores=0; // Compute similarity score. Retain the matches whose score is higher than minScore for(list<KeyFrame*>::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) { KeyFrame* pKFi = *lit; if(pKFi->mnLoopWords>minCommonWords) { nscores++; float si = mpVoc->score(pKF->mBowVec,pKFi->mBowVec); pKFi->mLoopScore = si; if(si>=minScore) lScoreAndMatch.push_back(make_pair(si,pKFi)); } } if(lScoreAndMatch.empty()) return vector<KeyFrame*>(); list<pair<float,KeyFrame*> > lAccScoreAndMatch; float bestAccScore = minScore; // Lets now accumulate score by covisibility // 对于上文筛选出来的pKFi,每一个都要抽取出自身的共视（共享地图点最多的前10帧） //关键帧分为一组，计算该组整体得分（与pKF比较的），记为bestAccScore; for(list<pair<float,KeyFrame*> >::iterator it=lScoreAndMatch.begin(), itend=lScoreAndMatch.end(); it!=itend; it++) { KeyFrame* pKFi = it->second; vector<KeyFrame*> vpNeighs = pKFi->GetBestCovisibilityKeyFrames(10); float bestScore = it->first; float accScore = it->first; KeyFrame* pBestKF = pKFi; for(vector<KeyFrame*>::iterator vit=vpNeighs.begin(), vend=vpNeighs.end(); vit!=vend; vit++) { KeyFrame* pKF2 = *vit; if(pKF2->mnLoopQuery==pKF->mnId && pKF2->mnLoopWords>minCommonWords) { accScore+=pKF2->mLoopScore; if(pKF2->mLoopScore>bestScore) { pBestKF=pKF2; bestScore = pKF2->mLoopScore; } } } lAccScoreAndMatch.push_back(make_pair(accScore,pBestKF)); if(accScore>bestAccScore) bestAccScore=accScore; } // Return all those keyframes with a score higher than 0.75*bestScore //所有组得分大于0.75*bestAccScore的，均当做闭环候选帧 float minScoreToRetain = 0.75f*bestAccScore; set<KeyFrame*> spAlreadyAddedKF; vector<KeyFrame*> vpLoopCandidates; vpLoopCandidates.reserve(lAccScoreAndMatch.size()); for(list<pair<float,KeyFrame*> >::iterator it=lAccScoreAndMatch.begin(), itend=lAccScoreAndMatch.end(); it!=itend; it++) { if(it->first>minScoreToRetain) { KeyFrame* pKFi = it->second; if(!spAlreadyAddedKF.count(pKFi)) { vpLoopCandidates.push_back(pKFi); spAlreadyAddedKF.insert(pKFi); } } } return vpLoopCandidates; } //检测重定位候选帧 //闭环候选帧和重定位候选帧之间的区别是闭环候选帧是从关键帧中选取 //重定位候选帧是从普通帧中选取 //其他的流程和闭环候选帧选取方式相同 vector<KeyFrame*> KeyFrameDatabase::DetectRelocalizationCandidates(Frame *F) { list<KeyFrame*> lKFsSharingWords; // Search all keyframes that share a word with current frame { unique_lock<mutex> lock(mMutex); for(DBoW2::BowVector::const_iterator vit=F->mBowVec.begin(), vend=F->mBowVec.end(); vit != vend; vit++) { list<KeyFrame*> &lKFs = mvInvertedFile[vit->first]; for(list<KeyFrame*>::iterator lit=lKFs.begin(), lend= lKFs.end(); lit!=lend; lit++) { KeyFrame* pKFi=*lit; if(pKFi->mnRelocQuery!=F->mnId) { pKFi->mnRelocWords=0; pKFi->mnRelocQuery=F->mnId; lKFsSharingWords.push_back(pKFi); } pKFi->mnRelocWords++; } } } if(lKFsSharingWords.empty()) return vector<KeyFrame*>(); // Only compare against those keyframes that share enough words int maxCommonWords=0; for(list<KeyFrame*>::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) { if((*lit)->mnRelocWords>maxCommonWords) maxCommonWords=(*lit)->mnRelocWords; } int minCommonWords = maxCommonWords*0.8f; list<pair<float,KeyFrame*> > lScoreAndMatch; int nscores=0; // Compute similarity score. for(list<KeyFrame*>::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) { KeyFrame* pKFi = *lit; if(pKFi->mnRelocWords>minCommonWords) { nscores++; float si = mpVoc->score(F->mBowVec,pKFi->mBowVec); pKFi->mRelocScore=si; lScoreAndMatch.push_back(make_pair(si,pKFi)); } } if(lScoreAndMatch.empty()) return vector<KeyFrame*>(); list<pair<float,KeyFrame*> > lAccScoreAndMatch; float bestAccScore = 0; // Lets now accumulate score by covisibility for(list<pair<float,KeyFrame*> >::iterator it=lScoreAndMatch.begin(), itend=lScoreAndMatch.end(); it!=itend; it++) { KeyFrame* pKFi = it->second; vector<KeyFrame*> vpNeighs = pKFi->GetBestCovisibilityKeyFrames(10); float bestScore = it->first; float accScore = bestScore; KeyFrame* pBestKF = pKFi; for(vector<KeyFrame*>::iterator vit=vpNeighs.begin(), vend=vpNeighs.end(); vit!=vend; vit++) { KeyFrame* pKF2 = *vit; if(pKF2->mnRelocQuery!=F->mnId) continue; accScore+=pKF2->mRelocScore; if(pKF2->mRelocScore>bestScore) { pBestKF=pKF2; bestScore = pKF2->mRelocScore; } } lAccScoreAndMatch.push_back(make_pair(accScore,pBestKF)); if(accScore>bestAccScore) bestAccScore=accScore; } // Return all those keyframes with a score higher than 0.75*bestScore float minScoreToRetain = 0.75f*bestAccScore; set<KeyFrame*> spAlreadyAddedKF; vector<KeyFrame*> vpRelocCandidates; vpRelocCandidates.reserve(lAccScoreAndMatch.size()); for(list<pair<float,KeyFrame*> >::iterator it=lAccScoreAndMatch.begin(), itend=lAccScoreAndMatch.end(); it!=itend; it++) { const float &si = it->first; if(si>minScoreToRetain) { KeyFrame* pKFi = it->second; if(!spAlreadyAddedKF.count(pKFi)) { vpRelocCandidates.push_back(pKFi); spAlreadyAddedKF.insert(pKFi); } } } return vpRelocCandidates; } } //namespace ORB_SLAM