ORBSLAM2源码学习(3) Map类和KeyFrameDataBase类
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2024-01-04 13:17:39
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将这两个类放在一起是因为代码都比较短,比较容易理解,否则一个类单独一篇那恐怕要写好久了...
1.Map类
Map类管理整个地图,因此其具有的属性是所有的地图点和关建针,即普通的帧是不管的,另外还有一些帧编号之类的东西,该类具有添加、删除关键帧的方法和添加、删除地图点的方法,另外就是一个返回属性的函数。直接上代码了。
#ifndef MAP_H
#define MAP_H
#include "MapPoint.h"
#include "KeyFrame.h"
#include <set>
#include <mutex>
namespace ORB_SLAM2
{
class MapPoint;
class KeyFrame;
class Map
{
public:
Map();
void AddKeyFrame(KeyFrame* pKF);
void AddMapPoint(MapPoint* pMP);
void EraseMapPoint(MapPoint* pMP);
void EraseKeyFrame(KeyFrame* pKF);
void SetReferenceMapPoints(const std::vector<MapPoint*> &vpMPs);
std::vector<KeyFrame*> GetAllKeyFrames();
std::vector<MapPoint*> GetAllMapPoints();
std::vector<MapPoint*> GetReferenceMapPoints();
long unsigned int MapPointsInMap();
long unsigned KeyFramesInMap();
long unsigned int GetMaxKFid();
void clear();
vector<KeyFrame*> mvpKeyFrameOrigins;
std::mutex mMutexMapUpdate;
// This avoid that two points are created simultaneously in separate threads (id conflict)
std::mutex mMutexPointCreation;
protected:
std::set<MapPoint*> mspMapPoints; ///< MapPoints
std::set<KeyFrame*> mspKeyFrames; ///< Keyframs
std::vector<MapPoint*> mvpReferenceMapPoints;
long unsigned int mnMaxKFid;
std::mutex mMutexMap;
};
} //namespace ORB_SLAM
#endif // MAP_H
#include "Map.h"
#include<mutex>
namespace ORB_SLAM2
{
Map::Map():mnMaxKFid(0)
{
}
//增加关键帧
void Map::AddKeyFrame(KeyFrame *pKF)
{
unique_lock<mutex> lock(mMutexMap);
mspKeyFrames.insert(pKF);
if(pKF->mnId>mnMaxKFid)
mnMaxKFid=pKF->mnId;
}
//增加地图点
void Map::AddMapPoint(MapPoint *pMP)
{
unique_lock<mutex> lock(mMutexMap);
mspMapPoints.insert(pMP);
}
//擦除地图点
void Map::EraseMapPoint(MapPoint *pMP)
{
unique_lock<mutex> lock(mMutexMap);
mspMapPoints.erase(pMP);
// TODO: This only erase the pointer.
// Delete the MapPoint
}
//擦除关键帧
void Map::EraseKeyFrame(KeyFrame *pKF)
{
unique_lock<mutex> lock(mMutexMap);
mspKeyFrames.erase(pKF);
// TODO: This only erase the pointer.
// Delete the MapPoint
}
//设置参考地图点
void Map::SetReferenceMapPoints(const vector<MapPoint *> &vpMPs)
{
unique_lock<mutex> lock(mMutexMap);
mvpReferenceMapPoints = vpMPs;
}
//获取所有关键帧
vector<KeyFrame*> Map::GetAllKeyFrames()
{
unique_lock<mutex> lock(mMutexMap);
return vector<KeyFrame*>(mspKeyFrames.begin(),mspKeyFrames.end());
}
//获取所有地图点
vector<MapPoint*> Map::GetAllMapPoints()
{
unique_lock<mutex> lock(mMutexMap);
return vector<MapPoint*>(mspMapPoints.begin(),mspMapPoints.end());
}
//返回地图中含有的地图点的个数
long unsigned int Map::MapPointsInMap()
{
unique_lock<mutex> lock(mMutexMap);
return mspMapPoints.size();
}
//返回地图中关键帧的个数
long unsigned int Map::KeyFramesInMap()
{
unique_lock<mutex> lock(mMutexMap);
return mspKeyFrames.size();
}
vector<MapPoint*> Map::GetReferenceMapPoints()
{
unique_lock<mutex> lock(mMutexMap);
return mvpReferenceMapPoints;
}
long unsigned int Map::GetMaxKFid()
{
unique_lock<mutex> lock(mMutexMap);
return mnMaxKFid;
}
void Map::clear()
{
for(set<MapPoint*>::iterator sit=mspMapPoints.begin(), send=mspMapPoints.end(); sit!=send; sit++)
delete *sit;
for(set<KeyFrame*>::iterator sit=mspKeyFrames.begin(), send=mspKeyFrames.end(); sit!=send; sit++)
delete *sit;
mspMapPoints.clear();
mspKeyFrames.clear();
mnMaxKFid = 0;
mvpReferenceMapPoints.clear();
mvpKeyFrameOrigins.clear();
}
} //namespace ORB_SLAM
2.KeyFrameDataBase类
这个类是关键帧的数据库,主要用于计算闭环检测和重定位时的候选帧,类中维护一个每个元素都是链表的vector容器,容器的每个元素是预先训练好的特征词汇,然后对应的连表上放的都是关键帧,包含这个词汇的关键帧。
#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; // 预先训练好的词典
// Inverted file
// mvInvertedFile[i]表示包含了第i个word id的所有关键帧
std::vector<list<KeyFrame*> > mvInvertedFile;
// Mutex
std::mutex mMutex;
};
} //namespace ORB_SLAM
#endif
具体实现
#include "KeyFrameDatabase.h"
#include "KeyFrame.h"
#include "Thirdparty/DBoW2/DBoW2/BowVector.h"
#include<mutex>
using namespace std;
namespace ORB_SLAM2
{
KeyFrameDatabase::KeyFrameDatabase (const ORBVocabulary &voc):
mpVoc(&voc)
{
mvInvertedFile.resize(voc.size()); // number of words
}
void KeyFrameDatabase::add(KeyFrame *pKF)
{
unique_lock<mutex> lock(mMutex);
// 为每一个word添加该KeyFrame
for(DBoW2::BowVector::const_iterator vit= pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit!=vend; vit++)
mvInvertedFile[vit->first].push_back(pKF);
}
// 关键帧被删除后,更新数据库
void KeyFrameDatabase::erase(KeyFrame* pKF)
{
unique_lock<mutex> lock(mMutex);
// Erase elements in the Inverse File for the entry
// 每一个KeyFrame包含多个words,遍历mvInvertedFile中的这些words,然后在word中删除该KeyFrame
for(DBoW2::BowVector::const_iterator vit=pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit!=vend; vit++)
{
// List of keyframes that share the word
list<KeyFrame*> &lKFs = mvInvertedFile[vit->first];
for(list<KeyFrame*>::iterator lit=lKFs.begin(), lend= lKFs.end(); lit!=lend; lit++)
{
if(pKF==*lit)
{
lKFs.erase(lit);
break;
}
}
}
}
void KeyFrameDatabase::clear()
{
mvInvertedFile.clear();// mvInvertedFile[i]表示包含了第i个word id的所有关键帧
mvInvertedFile.resize(mpVoc->size());// mpVoc:预先训练好的词典
}
// 在闭环检测中找到与该关键帧可能闭环的关键帧
vector<KeyFrame*> KeyFrameDatabase::DetectLoopCandidates(KeyFrame* pKF, float minScore)
{
// 提出所有与该pKF相连的KeyFrame,这些相连Keyframe都是局部相连,在闭环检测的时候将被剔除
// 即局部图中带有权重的关键帧
set<KeyFrame*> spConnectedKeyFrames = pKF->GetConnectedKeyFrames();
list<KeyFrame*> lKFsSharingWords;// 用于保存可能与pKF形成回环的候选帧
// Search all keyframes that share a word with current keyframes
// Discard keyframes connected to the query keyframe
// 1:找出和当前帧具有公共单词的所有不在局部图中的关键帧
{
unique_lock<mutex> lock(mMutex);
for(DBoW2::BowVector::const_iterator vit=pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit != vend; vit++)
{
// 提取所有包含该word的KeyFrame
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还没有标记
{
pKFi->mnLoopWords=0;
if(!spConnectedKeyFrames.count(pKFi))// 与pKF局部链接的关键帧不进入闭环候选帧
{
pKFi->mnLoopQuery=pKF->mnId;// pKFi标记为pKF的候选帧
lKFsSharingWords.push_back(pKFi);
}
}
pKFi->mnLoopWords++; // 记录pKFi与pKF具有相同词汇的个数
}
}
}
if(lKFsSharingWords.empty())
return vector<KeyFrame*>();
list<pair<float,KeyFrame*> > lScoreAndMatch;
// Only compare against those keyframes that share enough words
// 2:统计上一步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;
}
// 设置下限
int minCommonWords = maxCommonWords*0.8f;
int nscores=0;
// Compute similarity score. Retain the matches whose score is higher than minScore
// 3:遍历所有闭环候选帧,挑选出共有单词数大于minCommonWords且单词匹配度大于minScore的关键帧存入lScoreAndMatch
for(list<KeyFrame*>::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++)
{
KeyFrame* pKFi = *lit;
// 挑选出共有单词数大于minCommonWords
if(pKFi->mnLoopWords>minCommonWords)
{
nscores++;
// 计算相似度
float si = mpVoc->score(pKF->mBowVec,pKFi->mBowVec);
pKFi->mLoopScore = si;
// 只保留得分大于minScore的帧
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
// 将上一步中的每一帧的共视程度最高的前十个关键帧归为一组,计算累计得分
// 具体而言:lScoreAndMatch中每一个KeyFrame都把与自己共视程度较高的帧归为一组,
// 每一组会计算组得分并记录该组分数最高的KeyFrame,记录于lAccScoreAndMatch,最后保存这个组内得分最高的KeyFrame
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; // 该组最高分数
float accScore = it->first; // 该组累计得分
KeyFrame* pBestKF = pKFi; // 该组最高分数对应的关键帧
for(vector<KeyFrame*>::iterator vit=vpNeighs.begin(), vend=vpNeighs.end(); vit!=vend; vit++)
{
KeyFrame* pKF2 = *vit;
// pKF2在候选帧中且共同词汇数量大于阈值
if(pKF2->mnLoopQuery==pKF->mnId && pKF2->mnLoopWords>minCommonWords)
{
accScore+=pKF2->mLoopScore;
if(pKF2->mLoopScore>bestScore)// 统计得到组里分数最高的KeyFrame
{
pBestKF=pKF2;
bestScore = pKF2->mLoopScore;
}
}
}
// 保存组内得分最高的一帧
lAccScoreAndMatch.push_back(make_pair(accScore,pBestKF));
if(accScore>bestAccScore) // 记录所有组中组得分最高的组
bestAccScore=accScore;
}
// Return all those keyframes with a score higher than 0.75*bestScore
// 设置下限
float minScoreToRetain = 0.75f*bestAccScore;
set<KeyFrame*> spAlreadyAddedKF;
vector<KeyFrame*> vpLoopCandidates;
vpLoopCandidates.reserve(lAccScoreAndMatch.size());
// 得到组得分大于minScoreToRetain的组,得到组中分数最高的关键帧 0.75*bestScore
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;
}
// 在重定位中找到与该帧相似的关键帧
vector<KeyFrame*> KeyFrameDatabase::DetectRelocalizationCandidates(Frame *F)
{
list<KeyFrame*> lKFsSharingWords;
{
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还没有标记为pKF的候选帧
{
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;
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
代码中详细注释了计算闭环候选帧的过程,由于计算重定位候选帧的过程几乎类似,因此没有注释,两者区别在于:计算闭环候选帧时参考的是关键帧,而重定位时参考的是普通帧,因此也就没有像闭环那样剔除关键帧的局部图中的帧。