fix

include/ImuTypes.h

@@ -223,7 +223,7 @@ public:
 
 private:
     // Updated bias
-    Bias bu;
+    Bias bu;    //更新后的零偏
     // Dif between original and updated bias
     // This is used to compute the updated values of the preintegration
     Eigen::Matrix<float,6,1> db;

src/G2oTypes.cc

@@ -579,12 +579,12 @@ EdgeInertial::EdgeInertial(IMU::Preintegrated *pInt):JRg(pInt->JRg.cast<double>(
 void EdgeInertial::computeError()
 {
     // TODO Maybe Reintegrate inertial measurments when difference between linearization point and current estimate is too big
-    const VertexPose* VP1 = static_cast<const VertexPose*>(_vertices[0]);
+    const VertexPose* VP1 = static_cast<const VertexPose*>(_vertices[0]);           //位姿Ti
-    const VertexVelocity* VV1= static_cast<const VertexVelocity*>(_vertices[1]);
+    const VertexVelocity* VV1= static_cast<const VertexVelocity*>(_vertices[1]);    //速度vi
-    const VertexGyroBias* VG1= static_cast<const VertexGyroBias*>(_vertices[2]);
+    const VertexGyroBias* VG1= static_cast<const VertexGyroBias*>(_vertices[2]);    //零偏Bgi
-    const VertexAccBias* VA1= static_cast<const VertexAccBias*>(_vertices[3]);
+    const VertexAccBias* VA1= static_cast<const VertexAccBias*>(_vertices[3]);      //零偏Bai
-    const VertexPose* VP2 = static_cast<const VertexPose*>(_vertices[4]);
+    const VertexPose* VP2 = static_cast<const VertexPose*>(_vertices[4]);           //位姿Tj
-    const VertexVelocity* VV2 = static_cast<const VertexVelocity*>(_vertices[5]);
+    const VertexVelocity* VV2 = static_cast<const VertexVelocity*>(_vertices[5]);   //速度vj
     const IMU::Bias b1(VA1->estimate()[0],VA1->estimate()[1],VA1->estimate()[2],VG1->estimate()[0],VG1->estimate()[1],VG1->estimate()[2]);
     const Eigen::Matrix3d dR = mpInt->GetDeltaRotation(b1).cast<double>();
     const Eigen::Vector3d dV = mpInt->GetDeltaVelocity(b1).cast<double>();

src/ImuTypes.cc

@@ -242,7 +242,7 @@ void Preintegrated::Reintegrate()
  * 
  * @param[in] acceleration  加速度计数据
  * @param[in] angVel        陀螺仪数据
- * @param[in] dt            两帧之间时间差
+ * @param[in] dt            两图像 帧之间时间差
  */
 void Preintegrated::IntegrateNewMeasurement(const Eigen::Vector3f &acceleration, const Eigen::Vector3f &angVel, const float &dt)
 {

src/LocalMapping.cc

@@ -1547,10 +1547,8 @@ void LocalMapping::InitializeIMU(float priorG, float priorA, bool bFIBA)
         pKF = pKF->mPrevKF;
     }
     lpKF.push_front(pKF);
-    // 以相同内容再构建一个vector
+    // 同样内容再构建一个和lpKF一样的容器vpKF
     vector<KeyFrame*> vpKF(lpKF.begin(),lpKF.end());
-
-    // TODO 跟上面重复？
     if(vpKF.size()<nMinKF)
         return;
 

src/LoopClosing.cc

@@ -506,7 +506,8 @@ bool LoopClosing::NewDetectCommonRegions()
     //Merge candidates
     bool bMergeDetectedInKF = false;
     // Step 3.2 融合的时序几何校验: 注意初始化时mnMergeNumCoincidences=0, 所以可以先跳过看后面
-    // 如果成功验证总次数大于0
+    // mnMergeNumCoincidences表示成功校验总次数，初始化为0
+    // 会先经过后面共视几何校验，如果小于3，会进到如下判断开始时序几何校验
     if(mnMergeNumCoincidences > 0)
     {
         // Find from the last KF candidates
@@ -535,7 +536,7 @@ bool LoopClosing::NewDetectCommonRegions()
             mpMergeLastCurrentKF = mpCurrentKF;
             mg2oMergeSlw = gScw;
             mvpMergeMatchedMPs = vpMatchedMPs;
-            // 如果验证数大于等于3则为成功回环
+            // 如果验证数大于等于3则为成功
             mbMergeDetected = mnMergeNumCoincidences >= 3;
         }
         // 如果没找到共同区域(时序验证失败一次)
@@ -1550,8 +1551,8 @@ void LoopClosing::CorrectLoop()
     mpAtlas->InformNewBigChange();
 
     // Add loop edge
-    // Step 8. 添加当前帧与闭环匹配帧之间的边（这个连接关系不优化）
+    // Step 7：添加当前帧与闭环匹配帧之间的边（这个连接关系不优化）
-    // !感觉这两句话应该放在OptimizeEssentialGraph之前，因为OptimizeEssentialGraph的步骤4.2中有优化
+    // 它在下一次的Essential Graph里面使用
     mpLoopMatchedKF->AddLoopEdge(mpCurrentKF);
     mpCurrentKF->AddLoopEdge(mpLoopMatchedKF);
 
@@ -2112,8 +2113,8 @@ void LoopClosing::MergeLocal()
     bool bStop = false;
     // 为Local BA的接口, 把set转为vector
     // Step 7 在缝合(Welding)区域进行local BA 
-    vpLocalCurrentWindowKFs.clear();
+    vpLocalCurrentWindowKFs.clear();    //当前关键帧的窗口
-    vpMergeConnectedKFs.clear();
+    vpMergeConnectedKFs.clear();        //融合关键帧的窗口
     std::copy(spLocalWindowKFs.begin(), spLocalWindowKFs.end(), std::back_inserter(vpLocalCurrentWindowKFs));
     std::copy(spMergeConnectedKFs.begin(), spMergeConnectedKFs.end(), std::back_inserter(vpMergeConnectedKFs));
     if (mpTracker->mSensor==System::IMU_MONOCULAR || mpTracker->mSensor==System::IMU_STEREO || mpTracker->mSensor==System::IMU_RGBD)

src/Tracking.cc

@@ -3515,11 +3515,13 @@ bool Tracking::TrackLocalMap()
             if(!mbMapUpdated) //  && (mnMatchesInliers>30))
             {
                 Verbose::PrintMess("TLM: PoseInertialOptimizationLastFrame ", Verbose::VERBOSITY_DEBUG);
+                // 使用上一普通帧以及当前帧的视觉信息和IMU信息联合优化当前帧位姿、速度和IMU零偏
                 inliers = Optimizer::PoseInertialOptimizationLastFrame(&mCurrentFrame); // , !mpLastKeyFrame->GetMap()->GetIniertialBA1());
             }
             else
             {
                 Verbose::PrintMess("TLM: PoseInertialOptimizationLastKeyFrame ", Verbose::VERBOSITY_DEBUG);
+                // 使用上一关键帧以及当前帧的视觉信息和IMU信息联合优化当前帧位姿、速度和IMU零偏
                 inliers = Optimizer::PoseInertialOptimizationLastKeyFrame(&mCurrentFrame); // , !mpLastKeyFrame->GetMap()->GetIniertialBA1());
             }
         }