951 lines
37 KiB
C++
951 lines
37 KiB
C++
/**
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* This file is part of ORB-SLAM3
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*
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* Copyright (C) 2017-2021 Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
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* Copyright (C) 2014-2016 Raúl Mur-Artal, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
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*
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* ORB-SLAM3 is free software: you can redistribute it and/or modify it under the terms of the GNU General Public
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* License as published by the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* ORB-SLAM3 is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even
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* the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License along with ORB-SLAM3.
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* If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "KeyFrameDatabase.h"
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#include "KeyFrame.h"
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#include "Thirdparty/DBoW2/DBoW2/BowVector.h"
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#include <mutex>
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using namespace std;
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namespace ORB_SLAM3
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{
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// 构造函数
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KeyFrameDatabase::KeyFrameDatabase(const ORBVocabulary &voc) : mpVoc(&voc)
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{
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mvInvertedFile.resize(voc.size());
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}
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// 根据关键帧的BoW，更新数据库的倒排索引
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void KeyFrameDatabase::add(KeyFrame *pKF)
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{
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unique_lock<mutex> lock(mMutex);
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// 为每一个word添加该KeyFrame
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for (DBoW2::BowVector::const_iterator vit = pKF->mBowVec.begin(), vend = pKF->mBowVec.end(); vit != vend; vit++)
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mvInvertedFile[vit->first].push_back(pKF);
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}
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// 关键帧被删除后，更新数据库的倒排索引
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void KeyFrameDatabase::erase(KeyFrame *pKF)
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{
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unique_lock<mutex> lock(mMutex);
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// Erase elements in the Inverse File for the entry
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// 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
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for (DBoW2::BowVector::const_iterator vit = pKF->mBowVec.begin(), vend = pKF->mBowVec.end(); vit != vend; vit++)
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{
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// List of keyframes that share the word
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list<KeyFrame *> &lKFs = mvInvertedFile[vit->first];
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for (list<KeyFrame *>::iterator lit = lKFs.begin(), lend = lKFs.end(); lit != lend; lit++)
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{
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if (pKF == *lit)
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{
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lKFs.erase(lit);
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break;
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}
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}
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}
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}
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// 清空关键帧数据库
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void KeyFrameDatabase::clear()
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{
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mvInvertedFile.clear();
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mvInvertedFile.resize(mpVoc->size());
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}
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void KeyFrameDatabase::clearMap(Map *pMap)
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{
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unique_lock<mutex> lock(mMutex);
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// Erase elements in the Inverse File for the entry
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for (std::vector<list<KeyFrame *>>::iterator vit = mvInvertedFile.begin(), vend = mvInvertedFile.end(); vit != vend; vit++)
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{
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// List of keyframes that share the word
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list<KeyFrame *> &lKFs = *vit;
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for (list<KeyFrame *>::iterator lit = lKFs.begin(), lend = lKFs.end(); lit != lend;)
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{
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KeyFrame *pKFi = *lit;
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if (pMap == pKFi->GetMap())
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{
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lit = lKFs.erase(lit);
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// Dont delete the KF because the class Map clean all the KF when it is destroyed
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}
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else
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{
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++lit;
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}
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}
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}
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}
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/**
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* @brief 在闭环检测中找到与该关键帧可能闭环的关键帧（注意不和当前帧连接）
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* Step 1：找出和当前帧具有公共单词的所有关键帧，不包括与当前帧连接（也就是共视）的关键帧
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* Step 2：只和具有共同单词较多的（最大数目的80%以上）关键帧进行相似度计算
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* Step 3：计算上述候选帧对应的共视关键帧组的总得分，只取最高组得分75%以上的组
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* Step 4：得到上述组中分数最高的关键帧作为闭环候选关键帧
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* @param[in] pKF 需要闭环检测的关键帧
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* @param[in] minScore 候选闭环关键帧帧和当前关键帧的BoW相似度至少要大于minScore
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* @return vector<KeyFrame*> 闭环候选关键帧
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*/
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vector<KeyFrame *> KeyFrameDatabase::DetectLoopCandidates(KeyFrame *pKF, float minScore)
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{
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// 取出与当前关键帧相连（>15个共视地图点）的所有关键帧，这些相连关键帧都是局部相连，在闭环检测的时候将被剔除
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// 相连关键帧定义见 KeyFrame::UpdateConnections()
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set<KeyFrame *> spConnectedKeyFrames = pKF->GetConnectedKeyFrames();
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// 用于保存可能与当前关键帧形成闭环的候选帧（只要有相同的word，且不属于局部相连（共视）帧）
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list<KeyFrame *> lKFsSharingWords;
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// Search all keyframes that share a word with current keyframes
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// Discard keyframes connected to the query keyframe
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// Step 1：找出和当前帧具有公共单词的所有关键帧，不包括与当前帧连接（也就是共视）的关键帧
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{
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unique_lock<mutex> lock(mMutex);
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// words是检测图像是否匹配的枢纽，遍历该pKF的每一个word
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// mBowVec 内部实际存储的是std::map<WordId, WordValue>
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// WordId 和 WordValue 表示Word在叶子中的id 和权重
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for (DBoW2::BowVector::const_iterator vit = pKF->mBowVec.begin(), vend = pKF->mBowVec.end(); vit != vend; vit++)
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{
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// 提取所有包含该word的KeyFrame
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list<KeyFrame *> &lKFs = mvInvertedFile[vit->first];
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// 然后对这些关键帧展开遍历
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for (list<KeyFrame *>::iterator lit = lKFs.begin(), lend = lKFs.end(); lit != lend; lit++)
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{
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KeyFrame *pKFi = *lit;
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if (pKFi->GetMap() == pKF->GetMap()) // For consider a loop candidate it a candidate it must be in the same map
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{
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if (pKFi->mnLoopQuery != pKF->mnId)
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{
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// 还没有标记为pKF的闭环候选帧
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pKFi->mnLoopWords = 0;
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// 和当前关键帧共视的话不作为闭环候选帧
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if (!spConnectedKeyFrames.count(pKFi))
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{
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// 没有共视就标记作为闭环候选关键帧，放到lKFsSharingWords里
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pKFi->mnLoopQuery = pKF->mnId;
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lKFsSharingWords.push_back(pKFi);
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}
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}
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pKFi->mnLoopWords++; // 记录pKFi与pKF具有相同word的个数
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}
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}
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}
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}
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// 如果没有关键帧和这个关键帧具有相同的单词,那么就返回空
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if (lKFsSharingWords.empty())
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return vector<KeyFrame *>();
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list<pair<float, KeyFrame *>> lScoreAndMatch;
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// Only compare against those keyframes that share enough words
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// Step 2：统计上述所有闭环候选帧中与当前帧具有共同单词最多的单词数，用来决定相对阈值
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int maxCommonWords = 0;
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for (list<KeyFrame *>::iterator lit = lKFsSharingWords.begin(), lend = lKFsSharingWords.end(); lit != lend; lit++)
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{
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if ((*lit)->mnLoopWords > maxCommonWords)
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maxCommonWords = (*lit)->mnLoopWords;
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}
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// 确定最小公共单词数为最大公共单词数目的0.8倍
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int minCommonWords = maxCommonWords * 0.8f;
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int nscores = 0;
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// Compute similarity score. Retain the matches whose score is higher than minScore
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// Step 3：遍历上述所有闭环候选帧，挑选出共有单词数大于minCommonWords且单词匹配度大于minScore存入lScoreAndMatch
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for (list<KeyFrame *>::iterator lit = lKFsSharingWords.begin(), lend = lKFsSharingWords.end(); lit != lend; lit++)
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{
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KeyFrame *pKFi = *lit;
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// pKF只和具有共同单词较多（大于minCommonWords）的关键帧进行比较
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if (pKFi->mnLoopWords > minCommonWords)
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{
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nscores++;
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// 用mBowVec来计算两者的相似度得分
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float si = mpVoc->score(pKF->mBowVec, pKFi->mBowVec);
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pKFi->mLoopScore = si;
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if (si >= minScore)
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lScoreAndMatch.push_back(make_pair(si, pKFi));
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}
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}
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// 如果没有超过指定相似度阈值的，那么也就直接跳过去
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if (lScoreAndMatch.empty())
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return vector<KeyFrame *>();
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list<pair<float, KeyFrame *>> lAccScoreAndMatch;
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float bestAccScore = minScore;
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// Lets now accumulate score by covisibility
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// 单单计算当前帧和某一关键帧的相似性是不够的，这里将与关键帧相连（权值最高，共视程度最高）的前十个关键帧归为一组，计算累计得分
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// Step 4：计算上述候选帧对应的共视关键帧组的总得分，得到最高组得分bestAccScore，并以此决定阈值minScoreToRetain
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for (list<pair<float, KeyFrame *>>::iterator it = lScoreAndMatch.begin(), itend = lScoreAndMatch.end(); it != itend; it++)
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{
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KeyFrame *pKFi = it->second;
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vector<KeyFrame *> vpNeighs = pKFi->GetBestCovisibilityKeyFrames(10);
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float bestScore = it->first; // 该组最高分数
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float accScore = it->first; // 该组累计得分
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KeyFrame *pBestKF = pKFi; // 该组最高分数对应的关键帧
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for (vector<KeyFrame *>::iterator vit = vpNeighs.begin(), vend = vpNeighs.end(); vit != vend; vit++)
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{
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KeyFrame *pKF2 = *vit;
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// 只有pKF2也在闭环候选帧中，且公共单词数超过最小要求，才能贡献分数
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if (pKF2->mnLoopQuery == pKF->mnId && pKF2->mnLoopWords > minCommonWords)
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{
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accScore += pKF2->mLoopScore;
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// 统计得到组里分数最高的关键帧
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if (pKF2->mLoopScore > bestScore)
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{
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pBestKF = pKF2;
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bestScore = pKF2->mLoopScore;
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}
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}
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}
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lAccScoreAndMatch.push_back(make_pair(accScore, pBestKF));
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// 记录所有组中组得分最高的组，用于确定相对阈值
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if (accScore > bestAccScore)
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bestAccScore = accScore;
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}
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// Return all those keyframes with a score higher than 0.75*bestScore
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// 所有组中最高得分的0.75倍，作为最低阈值
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float minScoreToRetain = 0.75f * bestAccScore;
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set<KeyFrame *> spAlreadyAddedKF;
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vector<KeyFrame *> vpLoopCandidates;
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vpLoopCandidates.reserve(lAccScoreAndMatch.size());
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// Step 5：只取组得分大于阈值的组，得到组中分数最高的关键帧作为闭环候选关键帧
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for (list<pair<float, KeyFrame *>>::iterator it = lAccScoreAndMatch.begin(), itend = lAccScoreAndMatch.end(); it != itend; it++)
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{
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if (it->first > minScoreToRetain)
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{
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KeyFrame *pKFi = it->second;
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// spAlreadyAddedKF 是为了防止重复添加
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if (!spAlreadyAddedKF.count(pKFi))
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{
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vpLoopCandidates.push_back(pKFi);
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spAlreadyAddedKF.insert(pKFi);
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}
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}
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}
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return vpLoopCandidates;
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}
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void KeyFrameDatabase::DetectCandidates(KeyFrame *pKF, float minScore, vector<KeyFrame *> &vpLoopCand, vector<KeyFrame *> &vpMergeCand)
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{
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set<KeyFrame *> spConnectedKeyFrames = pKF->GetConnectedKeyFrames();
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list<KeyFrame *> lKFsSharingWordsLoop, lKFsSharingWordsMerge;
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// Search all keyframes that share a word with current keyframes
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// Discard keyframes connected to the query keyframe
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{
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unique_lock<mutex> lock(mMutex);
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for (DBoW2::BowVector::const_iterator vit = pKF->mBowVec.begin(), vend = pKF->mBowVec.end(); vit != vend; vit++)
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{
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list<KeyFrame *> &lKFs = mvInvertedFile[vit->first];
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for (list<KeyFrame *>::iterator lit = lKFs.begin(), lend = lKFs.end(); lit != lend; lit++)
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{
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KeyFrame *pKFi = *lit;
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if (pKFi->GetMap() == pKF->GetMap()) // For consider a loop candidate it a candidate it must be in the same map
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{
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if (pKFi->mnLoopQuery != pKF->mnId)
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{
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pKFi->mnLoopWords = 0;
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if (!spConnectedKeyFrames.count(pKFi))
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{
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pKFi->mnLoopQuery = pKF->mnId;
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lKFsSharingWordsLoop.push_back(pKFi);
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}
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}
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pKFi->mnLoopWords++;
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}
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else if (!pKFi->GetMap()->IsBad())
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{
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if (pKFi->mnMergeQuery != pKF->mnId)
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{
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pKFi->mnMergeWords = 0;
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if (!spConnectedKeyFrames.count(pKFi))
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{
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pKFi->mnMergeQuery = pKF->mnId;
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lKFsSharingWordsMerge.push_back(pKFi);
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}
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}
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pKFi->mnMergeWords++;
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}
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}
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}
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}
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if (lKFsSharingWordsLoop.empty() && lKFsSharingWordsMerge.empty())
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return;
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if (!lKFsSharingWordsLoop.empty())
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{
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list<pair<float, KeyFrame *>> lScoreAndMatch;
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// Only compare against those keyframes that share enough words
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int maxCommonWords = 0;
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for (list<KeyFrame *>::iterator lit = lKFsSharingWordsLoop.begin(), lend = lKFsSharingWordsLoop.end(); lit != lend; lit++)
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{
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if ((*lit)->mnLoopWords > maxCommonWords)
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maxCommonWords = (*lit)->mnLoopWords;
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}
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int minCommonWords = maxCommonWords * 0.8f;
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int nscores = 0;
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// Compute similarity score. Retain the matches whose score is higher than minScore
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for (list<KeyFrame *>::iterator lit = lKFsSharingWordsLoop.begin(), lend = lKFsSharingWordsLoop.end(); lit != lend; lit++)
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{
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KeyFrame *pKFi = *lit;
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if (pKFi->mnLoopWords > minCommonWords)
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{
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nscores++;
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float si = mpVoc->score(pKF->mBowVec, pKFi->mBowVec);
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pKFi->mLoopScore = si;
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if (si >= minScore)
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lScoreAndMatch.push_back(make_pair(si, pKFi));
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}
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}
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if (!lScoreAndMatch.empty())
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{
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list<pair<float, KeyFrame *>> lAccScoreAndMatch;
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float bestAccScore = minScore;
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// Lets now accumulate score by covisibility
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for (list<pair<float, KeyFrame *>>::iterator it = lScoreAndMatch.begin(), itend = lScoreAndMatch.end(); it != itend; it++)
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{
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KeyFrame *pKFi = it->second;
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vector<KeyFrame *> vpNeighs = pKFi->GetBestCovisibilityKeyFrames(10);
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float bestScore = it->first;
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float accScore = it->first;
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KeyFrame *pBestKF = pKFi;
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for (vector<KeyFrame *>::iterator vit = vpNeighs.begin(), vend = vpNeighs.end(); vit != vend; vit++)
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{
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KeyFrame *pKF2 = *vit;
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if (pKF2->mnLoopQuery == pKF->mnId && pKF2->mnLoopWords > minCommonWords)
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{
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accScore += pKF2->mLoopScore;
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if (pKF2->mLoopScore > bestScore)
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{
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pBestKF = pKF2;
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bestScore = pKF2->mLoopScore;
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}
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}
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}
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lAccScoreAndMatch.push_back(make_pair(accScore, pBestKF));
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if (accScore > bestAccScore)
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bestAccScore = accScore;
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}
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// Return all those keyframes with a score higher than 0.75*bestScore
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float minScoreToRetain = 0.75f * bestAccScore;
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set<KeyFrame *> spAlreadyAddedKF;
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vpLoopCand.reserve(lAccScoreAndMatch.size());
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for (list<pair<float, KeyFrame *>>::iterator it = lAccScoreAndMatch.begin(), itend = lAccScoreAndMatch.end(); it != itend; it++)
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{
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if (it->first > minScoreToRetain)
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{
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KeyFrame *pKFi = it->second;
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if (!spAlreadyAddedKF.count(pKFi))
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{
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vpLoopCand.push_back(pKFi);
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spAlreadyAddedKF.insert(pKFi);
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}
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}
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}
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}
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}
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if (!lKFsSharingWordsMerge.empty())
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{
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// cout << "BoW candidates: " << lKFsSharingWordsMerge.size() << endl;
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list<pair<float, KeyFrame *>> lScoreAndMatch;
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// Only compare against those keyframes that share enough words
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int maxCommonWords = 0;
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for (list<KeyFrame *>::iterator lit = lKFsSharingWordsMerge.begin(), lend = lKFsSharingWordsMerge.end(); lit != lend; lit++)
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{
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if ((*lit)->mnMergeWords > maxCommonWords)
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maxCommonWords = (*lit)->mnMergeWords;
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}
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// cout << "Max common words: " << maxCommonWords << endl;
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int minCommonWords = maxCommonWords * 0.8f;
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int nscores = 0;
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// Compute similarity score. Retain the matches whose score is higher than minScore
|
||
for (list<KeyFrame *>::iterator lit = lKFsSharingWordsMerge.begin(), lend = lKFsSharingWordsMerge.end(); lit != lend; lit++)
|
||
{
|
||
KeyFrame *pKFi = *lit;
|
||
|
||
if (pKFi->mnMergeWords > minCommonWords)
|
||
{
|
||
nscores++;
|
||
|
||
float si = mpVoc->score(pKF->mBowVec, pKFi->mBowVec);
|
||
// cout << "KF score: " << si << endl;
|
||
|
||
pKFi->mMergeScore = si;
|
||
if (si >= minScore)
|
||
lScoreAndMatch.push_back(make_pair(si, pKFi));
|
||
}
|
||
}
|
||
// cout << "BoW candidates2: " << lScoreAndMatch.size() << endl;
|
||
|
||
if (!lScoreAndMatch.empty())
|
||
{
|
||
list<pair<float, KeyFrame *>> lAccScoreAndMatch;
|
||
float bestAccScore = minScore;
|
||
|
||
// 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 = it->first;
|
||
KeyFrame *pBestKF = pKFi;
|
||
for (vector<KeyFrame *>::iterator vit = vpNeighs.begin(), vend = vpNeighs.end(); vit != vend; vit++)
|
||
{
|
||
KeyFrame *pKF2 = *vit;
|
||
if (pKF2->mnMergeQuery == pKF->mnId && pKF2->mnMergeWords > minCommonWords)
|
||
{
|
||
accScore += pKF2->mMergeScore;
|
||
if (pKF2->mMergeScore > bestScore)
|
||
{
|
||
pBestKF = pKF2;
|
||
bestScore = pKF2->mMergeScore;
|
||
}
|
||
}
|
||
}
|
||
|
||
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;
|
||
|
||
// cout << "Min score to retain: " << minScoreToRetain << endl;
|
||
|
||
set<KeyFrame *> spAlreadyAddedKF;
|
||
vpMergeCand.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))
|
||
{
|
||
vpMergeCand.push_back(pKFi);
|
||
spAlreadyAddedKF.insert(pKFi);
|
||
}
|
||
}
|
||
}
|
||
// cout << "Candidates: " << vpMergeCand.size() << endl;
|
||
}
|
||
}
|
||
|
||
//----
|
||
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;
|
||
pKFi->mnLoopQuery = -1;
|
||
pKFi->mnMergeQuery = -1;
|
||
}
|
||
}
|
||
}
|
||
|
||
void KeyFrameDatabase::DetectBestCandidates(KeyFrame *pKF, vector<KeyFrame *> &vpLoopCand, vector<KeyFrame *> &vpMergeCand, int nMinWords)
|
||
{
|
||
list<KeyFrame *> lKFsSharingWords;
|
||
set<KeyFrame *> spConnectedKF;
|
||
|
||
// Search all keyframes that share a word with current frame
|
||
{
|
||
unique_lock<mutex> lock(mMutex);
|
||
|
||
spConnectedKF = pKF->GetConnectedKeyFrames();
|
||
|
||
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 (spConnectedKF.find(pKFi) != spConnectedKF.end())
|
||
{
|
||
continue;
|
||
}
|
||
if (pKFi->mnPlaceRecognitionQuery != pKF->mnId)
|
||
{
|
||
pKFi->mnPlaceRecognitionWords = 0;
|
||
pKFi->mnPlaceRecognitionQuery = pKF->mnId;
|
||
lKFsSharingWords.push_back(pKFi);
|
||
}
|
||
pKFi->mnPlaceRecognitionWords++;
|
||
}
|
||
}
|
||
}
|
||
if (lKFsSharingWords.empty())
|
||
return;
|
||
|
||
// 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)->mnPlaceRecognitionWords > maxCommonWords)
|
||
maxCommonWords = (*lit)->mnPlaceRecognitionWords;
|
||
}
|
||
|
||
int minCommonWords = maxCommonWords * 0.8f;
|
||
|
||
if (minCommonWords < nMinWords)
|
||
{
|
||
minCommonWords = nMinWords;
|
||
}
|
||
|
||
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->mnPlaceRecognitionWords > minCommonWords)
|
||
{
|
||
nscores++;
|
||
float si = mpVoc->score(pKF->mBowVec, pKFi->mBowVec);
|
||
pKFi->mPlaceRecognitionScore = si;
|
||
lScoreAndMatch.push_back(make_pair(si, pKFi));
|
||
}
|
||
}
|
||
|
||
if (lScoreAndMatch.empty())
|
||
return;
|
||
|
||
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->mnPlaceRecognitionQuery != pKF->mnId)
|
||
continue;
|
||
|
||
accScore += pKF2->mPlaceRecognitionScore;
|
||
if (pKF2->mPlaceRecognitionScore > bestScore)
|
||
{
|
||
pBestKF = pKF2;
|
||
bestScore = pKF2->mPlaceRecognitionScore;
|
||
}
|
||
}
|
||
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;
|
||
vpLoopCand.reserve(lAccScoreAndMatch.size());
|
||
vpMergeCand.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))
|
||
{
|
||
if (pKF->GetMap() == pKFi->GetMap())
|
||
{
|
||
vpLoopCand.push_back(pKFi);
|
||
}
|
||
else
|
||
{
|
||
vpMergeCand.push_back(pKFi);
|
||
}
|
||
spAlreadyAddedKF.insert(pKFi);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
bool compFirst(const pair<float, KeyFrame *> &a, const pair<float, KeyFrame *> &b)
|
||
{
|
||
return a.first > b.first;
|
||
}
|
||
|
||
/**
|
||
* @brief 找到N个融合候选N个回环候选
|
||
*
|
||
* @param[in] pKF 当前关键帧(我们要寻找这个关键帧的回环候选帧和融合候选帧)
|
||
* @param[out] vpLoopCand 记录找到的回环候选帧
|
||
* @param[out] vpMergeCand 记录找到的融合候选帧
|
||
* @param[in] nNumCandidates 期望的候选数目,即回环和候选分别应该有多少个
|
||
*/
|
||
void KeyFrameDatabase::DetectNBestCandidates(KeyFrame *pKF, vector<KeyFrame *> &vpLoopCand, vector<KeyFrame *> &vpMergeCand, int nNumCandidates)
|
||
{
|
||
// Step 1统计与当前关键帧有相同单词的关键帧
|
||
list<KeyFrame *> lKFsSharingWords;
|
||
// set<KeyFrame*> spInsertedKFsSharing;
|
||
// 当前关键帧的共视关键帧(避免将当前关键帧的共视关键帧加入回环检测)
|
||
set<KeyFrame *> spConnectedKF;
|
||
|
||
// Search all keyframes that share a word with current frame
|
||
{
|
||
unique_lock<mutex> lock(mMutex);
|
||
// 拿到当前关键帧的共视关键帧
|
||
spConnectedKF = pKF->GetConnectedKeyFrames();
|
||
|
||
// 遍历当前关键帧bow向量的每个单词
|
||
for (DBoW2::BowVector::const_iterator vit = pKF->mBowVec.begin(), vend = pKF->mBowVec.end(); vit != vend; vit++)
|
||
{ // 拿到当前单词的逆向索引(所有有当前单词的关键帧)
|
||
list<KeyFrame *> &lKFs = mvInvertedFile[vit->first];
|
||
// 遍历每个有该单词的关键帧
|
||
for (list<KeyFrame *>::iterator lit = lKFs.begin(), lend = lKFs.end(); lit != lend; lit++)
|
||
{
|
||
KeyFrame *pKFi = *lit;
|
||
/*if(spConnectedKF.find(pKFi) != spConnectedKF.end())
|
||
{
|
||
continue;
|
||
}*/
|
||
// 如果此关键帧没有被当前关键帧访问过(防止重复添加)
|
||
if (pKFi->mnPlaceRecognitionQuery != pKF->mnId)
|
||
{
|
||
// 初始化公共单词数为0
|
||
pKFi->mnPlaceRecognitionWords = 0;
|
||
// 如果该关键帧不是当前关键帧的共视关键帧
|
||
if (!spConnectedKF.count(pKFi))
|
||
{
|
||
// 标记该关键帧被当前关键帧访问到（也就是有公共单词）
|
||
pKFi->mnPlaceRecognitionQuery = pKF->mnId;
|
||
// 把当前关键帧添加到有公共单词的关键帧列表中
|
||
lKFsSharingWords.push_back(pKFi);
|
||
}
|
||
}
|
||
// 递增该关键帧与当前关键帧的公共单词数
|
||
pKFi->mnPlaceRecognitionWords++;
|
||
/*if(spInsertedKFsSharing.find(pKFi) == spInsertedKFsSharing.end())
|
||
{
|
||
lKFsSharingWords.push_back(pKFi);
|
||
spInsertedKFsSharing.insert(pKFi);
|
||
}*/
|
||
}
|
||
}
|
||
}
|
||
// 如果没有有公共单词的关键帧,直接返回
|
||
if (lKFsSharingWords.empty())
|
||
return;
|
||
|
||
// Only compare against those keyframes that share enough words
|
||
// Step 2 统计所有候选帧中与当前关键帧的公共单词数最多的单词数maxCommonWords,并筛选
|
||
int maxCommonWords = 0;
|
||
for (list<KeyFrame *>::iterator lit = lKFsSharingWords.begin(), lend = lKFsSharingWords.end(); lit != lend; lit++)
|
||
{
|
||
if ((*lit)->mnPlaceRecognitionWords > maxCommonWords)
|
||
maxCommonWords = (*lit)->mnPlaceRecognitionWords;
|
||
}
|
||
// 取0.8倍为阀值
|
||
int minCommonWords = maxCommonWords * 0.8f;
|
||
// 这里的pair是 <相似度,候选帧的指针> : 记录所有大于minCommonWords的候选帧与当前关键帧的相似度
|
||
list<pair<float, KeyFrame *>> lScoreAndMatch;
|
||
// 只是个统计变量,貌似没有用到
|
||
int nscores = 0;
|
||
|
||
// Compute similarity score.
|
||
// 对所有大于minCommonWords的候选帧计算相似度
|
||
|
||
// 遍历所有有公共单词的候选帧
|
||
for (list<KeyFrame *>::iterator lit = lKFsSharingWords.begin(), lend = lKFsSharingWords.end(); lit != lend; lit++)
|
||
{
|
||
KeyFrame *pKFi = *lit;
|
||
// 如果当前帧的公共单词数大于minCommonWords
|
||
if (pKFi->mnPlaceRecognitionWords > minCommonWords)
|
||
{
|
||
nscores++; //未使用
|
||
// 计算相似度
|
||
float si = mpVoc->score(pKF->mBowVec, pKFi->mBowVec);
|
||
// 记录该候选帧与当前帧的相似度
|
||
pKFi->mPlaceRecognitionScore = si;
|
||
// 记录到容器里, 每个元素是<相似度,候选帧的指针>
|
||
lScoreAndMatch.push_back(make_pair(si, pKFi));
|
||
}
|
||
}
|
||
// 如果为空,直接返回,表示没有符合上述条件的关键帧
|
||
if (lScoreAndMatch.empty())
|
||
return;
|
||
// Step 3 : 用小组得分排序得到top3总分里最高分的关键帧,作为候选帧
|
||
// 统计以组为单位的累计相似度和组内相似度最高的关键帧, 每个pair为<小组总相似度,组内相似度最高的关键帧指针>
|
||
list<pair<float, KeyFrame *>> lAccScoreAndMatch;
|
||
float bestAccScore = 0;
|
||
|
||
// Lets now accumulate score by covisibility
|
||
// 变量所有被lScoreAndMatch记录的pair <相似度,候选关键帧>
|
||
for (list<pair<float, KeyFrame *>>::iterator it = lScoreAndMatch.begin(), itend = lScoreAndMatch.end(); it != itend; it++)
|
||
{
|
||
// 候选关键帧
|
||
KeyFrame *pKFi = it->second;
|
||
// 与候选关键帧共视关系最好的10个关键帧
|
||
vector<KeyFrame *> vpNeighs = pKFi->GetBestCovisibilityKeyFrames(10);
|
||
// 初始化最大相似度为该候选关键帧自己的相似度
|
||
float bestScore = it->first;
|
||
// 初始化小组累计得分为改候选关键帧自己的相似度
|
||
float accScore = bestScore;
|
||
// 初始化组内相似度最高的帧为该候选关键帧本身
|
||
KeyFrame *pBestKF = pKFi;
|
||
// 遍历与当前关键帧共视关系最好的10帧
|
||
for (vector<KeyFrame *>::iterator vit = vpNeighs.begin(), vend = vpNeighs.end(); vit != vend; vit++)
|
||
{
|
||
KeyFrame *pKF2 = *vit;
|
||
// 如果该关键帧没有被当前关键帧访问过(也就是没有公共单词)则跳过
|
||
if (pKF2->mnPlaceRecognitionQuery != pKF->mnId)
|
||
continue;
|
||
// 累加小组总分
|
||
accScore += pKF2->mPlaceRecognitionScore;
|
||
// 如果大于组内最高分,则更新当前最高分记录
|
||
if (pKF2->mPlaceRecognitionScore > bestScore)
|
||
{
|
||
pBestKF = pKF2;
|
||
bestScore = pKF2->mPlaceRecognitionScore;
|
||
}
|
||
}
|
||
// 统计以组为单位的累计相似度和组内相似度最高的关键帧, 每个pair为<小组总相似度,组内相似度最高的关键帧指针>
|
||
lAccScoreAndMatch.push_back(make_pair(accScore, pBestKF));
|
||
// 统计最高得分, 这个bestAccSocre没有用到
|
||
if (accScore > bestAccScore)
|
||
bestAccScore = accScore;
|
||
}
|
||
|
||
// cout << "Amount of candidates: " << lAccScoreAndMatch.size() << endl;
|
||
// 按相似度从大到小排序
|
||
lAccScoreAndMatch.sort(compFirst);
|
||
// 最后返回的变量, 记录回环的候选帧
|
||
vpLoopCand.reserve(nNumCandidates);
|
||
// 最后返回的变量, 记录融合候选帧
|
||
vpMergeCand.reserve(nNumCandidates);
|
||
// 避免重复添加
|
||
set<KeyFrame *> spAlreadyAddedKF;
|
||
// cout << "Candidates in score order " << endl;
|
||
// for(list<pair<float,KeyFrame*> >::iterator it=lAccScoreAndMatch.begin(), itend=lAccScoreAndMatch.end(); it!=itend; it++)
|
||
int i = 0;
|
||
list<pair<float, KeyFrame *>>::iterator it = lAccScoreAndMatch.begin();
|
||
// 遍历lAccScoreAndMatch中所有的pair, 每个pair为<小组总相似度,组内相似度最高的关键帧指针>，nNumCandidates默认为3
|
||
while (i < lAccScoreAndMatch.size() && (vpLoopCand.size() < nNumCandidates || vpMergeCand.size() < nNumCandidates))
|
||
{
|
||
// cout << "Accum score: " << it->first << endl;
|
||
// 拿到候选关键帧的指针
|
||
KeyFrame *pKFi = it->second;
|
||
if (pKFi->isBad())
|
||
continue;
|
||
|
||
// 如果没有被重复添加
|
||
if (!spAlreadyAddedKF.count(pKFi))
|
||
{
|
||
// 如果候选帧与当前关键帧在同一个地图里,且候选者数量还不足够
|
||
if (pKF->GetMap() == pKFi->GetMap() && vpLoopCand.size() < nNumCandidates)
|
||
{
|
||
// 添加到回环候选帧里
|
||
vpLoopCand.push_back(pKFi);
|
||
}
|
||
// 如果候选者与当前关键帧不再同一个地图里, 且候选者数量还不足够, 且候选者所在地图不是bad
|
||
else if (pKF->GetMap() != pKFi->GetMap() && vpMergeCand.size() < nNumCandidates && !pKFi->GetMap()->IsBad())
|
||
{
|
||
// 添加到融合候选帧里
|
||
vpMergeCand.push_back(pKFi);
|
||
}
|
||
// 防止重复添加
|
||
spAlreadyAddedKF.insert(pKFi);
|
||
}
|
||
i++;
|
||
it++;
|
||
}
|
||
}
|
||
|
||
vector<KeyFrame *> KeyFrameDatabase::DetectRelocalizationCandidates(Frame *F, Map *pMap)
|
||
{
|
||
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 (pKFi->GetMap() != pMap)
|
||
continue;
|
||
if (!spAlreadyAddedKF.count(pKFi))
|
||
{
|
||
vpRelocCandidates.push_back(pKFi);
|
||
spAlreadyAddedKF.insert(pKFi);
|
||
}
|
||
}
|
||
}
|
||
|
||
return vpRelocCandidates;
|
||
}
|
||
|
||
void KeyFrameDatabase::SetORBVocabulary(ORBVocabulary *pORBVoc)
|
||
{
|
||
ORBVocabulary **ptr;
|
||
ptr = (ORBVocabulary **)(&mpVoc);
|
||
*ptr = pORBVoc;
|
||
|
||
mvInvertedFile.clear();
|
||
mvInvertedFile.resize(mpVoc->size());
|
||
}
|
||
|
||
} // namespace ORB_SLAM
|