6.2 代码解析
这部分代码在estimator::processImage()最后面。初始化部分的代码虽然生命周期比较短，但是，代码量巨大！主要分成2部分，第一部分是纯视觉SfM优化滑窗内的位姿，然后在融合IMU信息，按照理论部分优化各个状态量。
<code class="prism language-cpp has-numbering"> <span class="token keyword">if</span> <span class="token punctuation">(</span>solver_flag <span class="token operator">==</span> INITIAL<span class="token punctuation">)</span><span class="token comment">//进行初始化</span>
<span class="token punctuation">{</span>
<span class="token keyword">if</span> <span class="token punctuation">(</span>frame_count <span class="token operator">==</span> WINDOW_SIZE<span class="token punctuation">)</span>
<span class="token punctuation">{</span>
<span class="token keyword">bool</span> result <span class="token operator">=</span> <span class="token boolean">false</span><span class="token punctuation">;</span>
<span class="token keyword">if</span><span class="token punctuation">(</span> ESTIMATE_EXTRINSIC <span class="token operator">!=</span> <span class="token number">2</span> <span class="token operator">&&</span> <span class="token punctuation">(</span>header<span class="token punctuation">.</span>stamp<span class="token punctuation">.</span><span class="token function">toSec</span><span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token operator">-</span> initial_timestamp<span class="token punctuation">)</span> <span class="token operator">></span> <span class="token number">0.1</span><span class="token punctuation">)</span>
<span class="token punctuation">{</span><span class="token comment">//确保有足够的frame参与初始化，有外参，且当前帧时间戳大于初始化时间戳+0.1秒</span>
result <span class="token operator">=</span> <span class="token function">initialStructure</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//执行视觉惯性联合初始化</span>
initial_timestamp <span class="token operator">=</span> header<span class="token punctuation">.</span>stamp<span class="token punctuation">.</span><span class="token function">toSec</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//更新初始化时间戳</span>
<span class="token punctuation">}</span>
<span class="token keyword">if</span><span class="token punctuation">(</span>result<span class="token punctuation">)</span><span class="token comment">//初始化成功则进行一次非线性优化</span>
<span class="token punctuation">{</span>
solver_flag <span class="token operator">=</span> NON_LINEAR<span class="token punctuation">;</span><span class="token comment">//进行非线性优化</span>
<span class="token function">solveOdometry</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//执行非线性优化具体函数solveOdometry()</span>
<span class="token function">slideWindow</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span>
f_manager<span class="token punctuation">.</span><span class="token function">removeFailures</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span>
<span class="token function">ROS_INFO</span><span class="token punctuation">(</span><span class="token string">"Initialization finish!"</span><span class="token punctuation">)</span><span class="token punctuation">;</span>
last_R <span class="token operator">=</span> Rs<span class="token punctuation">[</span>WINDOW_SIZE<span class="token punctuation">]</span><span class="token punctuation">;</span><span class="token comment">//得到当前帧与第一帧的位姿</span>
last_P <span class="token operator">=</span> Ps<span class="token punctuation">[</span>WINDOW_SIZE<span class="token punctuation">]</span><span class="token punctuation">;</span>
last_R0 <span class="token operator">=</span> Rs<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">;</span>
last_P0 <span class="token operator">=</span> Ps<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">;</span>
<span class="token punctuation">}</span>
<span class="token keyword">else</span>
<span class="token function">slideWindow</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//不成功则进行滑窗操作</span>
<span class="token punctuation">}</span>
<span class="token keyword">else</span>
<span class="token comment">//TODO 再看看这个值是怎么变的</span>
<span class="token punctuation">;</span><span class="token comment">//图像帧数量+1</span>
<span class="token punctuation">}</span>
</code>
6.3 initialStructure()
这是一个相当大的函数，而且多层套娃。而且原理上讲的初始化，包括纯视觉SfM，视觉和IMU的松耦合，都是在这个部分里面。
6.3.1 确保IMU有足够的excitation(可选)
这一部分的思想就是通过计算滑窗内所有帧的线加速度的标准差，判断IMU是否有充分运动激励，以判断是否进行初始化。 一上来就出现all_image_frame这个数据结构，见5.2-2，它包含了滑窗内所有帧的视觉和IMU信息，它是一个hash，以时间戳为索引。 (1)第一次循环，求出滑窗内的平均线加速度
<code class="prism language-cpp has-numbering">Vector3d sum_g<span class="token punctuation">;</span>
<span class="token keyword">for</span> <span class="token punctuation">(</span>frame_it <span class="token operator">=</span> all_image_frame<span class="token punctuation">.</span><span class="token function">begin</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> frame_it<span class="token operator">++</span><span class="token punctuation">;</span> frame_it <span class="token operator">!=</span> all_image_frame<span class="token punctuation">.</span><span class="token function">end</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span> frame_it<span class="token operator">++</span><span class="token punctuation">)</span>
<span class="token punctuation">{</span>
<span class="token keyword">double</span> dt <span class="token operator">=</span> frame_it<span class="token operator">-</span><span class="token operator">></span>second<span class="token punctuation">.</span>pre_integration<span class="token operator">-</span><span class="token operator">></span>sum_dt<span class="token punctuation">;</span><span class="token comment">//time for bk to bk+1</span>
Vector3d tmp_g <span class="token operator">=</span> frame_it<span class="token operator">-</span><span class="token operator">></span>second<span class="token punctuation">.</span>pre_integration<span class="token operator">-</span><span class="token operator">></span>delta_v <span class="token operator">/</span> dt<span class="token punctuation">;</span>
sum_g <span class="token operator">+</span><span class="token operator">=</span> tmp_g<span class="token punctuation">;</span>
<span class="token punctuation">}</span>
Vector3d aver_g<span class="token punctuation">;</span>
aver_g <span class="token operator">=</span> sum_g <span class="token operator">*</span> <span class="token number">1.0</span> <span class="token operator">/</span> <span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token keyword">int</span><span class="token punctuation">)</span>all_image_frame<span class="token punctuation">.</span><span class="token function">size</span><span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token operator">-</span> <span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">;</span>
</code>
(2)第二次循环，求出滑窗内的线加速度的标准差
<code class="prism language-cpp has-numbering"><span class="token keyword">double</span> var <span class="token operator">=</span> <span class="token number">0</span><span class="token punctuation">;</span>
<span class="token keyword">for</span> <span class="token punctuation">(</span>frame_it <span class="token operator">=</span> all_image_frame<span class="token punctuation">.</span><span class="token function">begin</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> frame_it<span class="token operator">++</span><span class="token punctuation">;</span> frame_it <span class="token operator">!=</span> all_image_frame<span class="token punctuation">.</span><span class="token function">end</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span> frame_it<span class="token operator">++</span><span class="token punctuation">)</span>
<span class="token punctuation">{</span>
<span class="token keyword">double</span> dt <span class="token operator">=</span> frame_it<span class="token operator">-</span><span class="token operator">></span>second<span class="token punctuation">.</span>pre_integration<span class="token operator">-</span><span class="token operator">></span>sum_dt<span class="token punctuation">;</span>
Vector3d tmp_g <span class="token operator">=</span> frame_it<span class="token operator">-</span><span class="token operator">></span>second<span class="token punctuation">.</span>pre_integration<span class="token operator">-</span><span class="token operator">></span>delta_v <span class="token operator">/</span> dt<span class="token punctuation">;</span>
var <span class="token operator">+</span><span class="token operator">=</span> <span class="token punctuation">(</span>tmp_g <span class="token operator">-</span> aver_g<span class="token punctuation">)</span><span class="token punctuation">.</span><span class="token function">transpose</span><span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token operator">*</span> <span class="token punctuation">(</span>tmp_g <span class="token operator">-</span> aver_g<span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//计算加速度的方差</span>
<span class="token comment">//cout << "frame g " << tmp_g.transpose() << endl;</span>
<span class="token punctuation">}</span>
var <span class="token operator">=</span> <span class="token function">sqrt</span><span class="token punctuation">(</span>var <span class="token operator">/</span> <span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token keyword">int</span><span class="token punctuation">)</span>all_image_frame<span class="token punctuation">.</span><span class="token function">size</span><span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token operator">-</span> <span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//计算加速度的标准差</span>
<span class="token comment">//ROS_WARN("IMU variation %f!", var);</span>
<span class="token keyword">if</span><span class="token punctuation">(</span>var <span class="token operator"><</span> <span class="token number">0.25</span><span class="token punctuation">)</span>
<span class="token punctuation">{</span>
<span class="token function">ROS_INFO</span><span class="token punctuation">(</span><span class="token string">"IMU excitation not enouth!"</span><span class="token punctuation">)</span><span class="token punctuation">;</span>
<span class="token comment">//return false;</span>
<span class="token punctuation">}</span>
</code>
6.3.2 将f_manager中的所有feature在所有帧的归一化坐标保存到vector sfm_f中(辅助) (1)一上来就定义了几个容器，分别是：
<code class="prism language-cpp has-numbering">Quaterniond Q<span class="token punctuation">[</span>frame_count <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">;</span> Vector3d T<span class="token punctuation">[</span>frame_count <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">;</span> map<span class="token operator"><</span><span class="token keyword">int</span><span class="token punctuation">,</span> Vector3d<span class="token operator">></span> sfm_tracked_points<span class="token punctuation">;</span> vector<span class="token operator"><</span>SFMFeature<span class="token operator">></span> sfm_f<span class="token punctuation">;</span> </code>
这块有2个需要注意的地方， 就是为什么容量是frame_count + 1？因为滑窗的容量是10，再加上当前最新帧，所以需要储存11帧的值！ 然后出现了一个新的数据结构，我们看一下：
<code class="prism language-cpp has-numbering">数据结构<span class="token operator">:</span> vector<span class="token operator"><</span>SFMFeature<span class="token operator">></span> sfm_f
它定义在initial<span class="token operator">/</span>initial_sfm<span class="token punctuation">.</span>h中，
<span class="token keyword">struct</span> SFMFeature
<span class="token punctuation">{</span>
<span class="token keyword">bool</span> state<span class="token punctuation">;</span><span class="token comment">//状态（是否被三角化）</span>
<span class="token keyword">int</span> id<span class="token punctuation">;</span>
vector<span class="token operator"><</span>pair<span class="token operator"><</span><span class="token keyword">int</span><span class="token punctuation">,</span>Vector2d<span class="token operator">>></span> observation<span class="token punctuation">;</span><span class="token comment">//所有观测到该特征点的 图像帧ID 和 特征点在这个图像帧的归一化坐标</span>
<span class="token keyword">double</span> position<span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">;</span><span class="token comment">//在帧l下的空间坐标</span>
<span class="token keyword">double</span> depth<span class="token punctuation">;</span><span class="token comment">//深度</span>
<span class="token punctuation">}</span><span class="token punctuation">;</span>
</code>
可以发现，它存放着一个特征点的所有信息。容器定义完了，接下来就是往容器里放数据。 (2) 往容器里放数据
for (auto &it_per_id : f_manager.feature)//对于滑窗中出现的 所有特征点
{
int imu_j = it_per_id.start_frame - 1;//从start_frame开始帧编号
SFMFeature tmp_feature;
tmp_feature.state = false;//状态（是否被三角化）
tmp_feature.id = it_per_id.feature_id;//特征点id
for (auto &it_per_frame : it_per_id.feature_per_frame)//对于当前特征点 在每一帧的坐标
{
imu_j++;//帧编号+1
Vector3d pts_j = it_per_frame.point;//当前特征在编号为imu_j++的帧的归一化坐标
tmp_feature.observation.push_back(make_pair(imu_j, Eigen::Vector2d{pts_j.x(), pts_j.y()}));//把当前特征在当前帧的坐标和当前帧的编号配上对
}//tmp_feature.observation里面存放着的一直是同一个特征，每一个pair是这个特征在 不同帧号 中的 归一化坐标
sfm_f.push_back(tmp_feature);//sfm_f里面存放着是不同特征
}
在这里，为什么要多此一举构造一个sfm_f而不是直接使用f_manager呢？ 我的理解，是因为f_manager的信息量大于SfM所需的信息量(f_manager包含了大量的像素信息)，而且不同的数据结构是为了不同的业务服务的，所以在这里作者专门为SfM设计了一个全新的数据结构sfm_f，专业化服务。 6.3.3 在滑窗(0-9)中找到第一个满足要求的帧(第l帧)，它与最新一帧(frame_count=10)有足够的共视点和平行度，并求出这两帧之间的相对位置变化关系 (1)定义容器
<code class="prism language-cpp has-numbering">Matrix3d relative_R<span class="token punctuation">;</span> Vector3d relative_T<span class="token punctuation">;</span> <span class="token keyword">int</span> l<span class="token punctuation">;</span> <span class="token comment">//滑窗中满足与最新帧视差关系的那一帧的帧号</span> </code>
(2)两帧之间的视差判断,并得到两帧之间的相对位姿变化关系
if (!relativePose(relative_R, relative_T, l))
{//这里的第L帧是从第一帧开始到滑动窗口中第一个满足与当前帧的平均视差足够大的帧l，会作为 参考帧 到下面的全局sfm使用，得到的Rt为当前帧到第l帧的坐标系变换Rt
ROS_INFO("Not enough features or parallax; Move device around");
return false;
}
这里，又出现了个新的函数relativePose()，这个函数也是6.3.3的主要功能，进去看一下： 首先，搞清楚这个函数是要干什么事情？ a.计算滑窗内的每一帧(0-9)与最新一帧(10)之间的视差，直到找出第一个满足要求的帧，作为我们的第l帧：
<code class="prism language-cpp has-numbering"> Estimator<span class="token operator">::</span><span class="token function">relativePose</span><span class="token punctuation">(</span>Matrix3d <span class="token operator">&</span>relative_R<span class="token punctuation">,</span> Vector3d <span class="token operator">&</span>relative_T<span class="token punctuation">,</span> <span class="token keyword">int</span> <span class="token operator">&</span>l<span class="token punctuation">)</span>
<span class="token punctuation">{</span> <span class="token comment">//output array R,t</span>
<span class="token comment">// find previous frame which contians enough correspondance and parallex with newest frame</span>
<span class="token keyword">for</span> <span class="token punctuation">(</span><span class="token keyword">int</span> i <span class="token operator">=</span> <span class="token number">0</span><span class="token punctuation">;</span> i <span class="token operator"><</span> WINDOW_SIZE<span class="token punctuation">;</span> i<span class="token operator">++</span><span class="token punctuation">)</span>
<span class="token punctuation">{</span><span class="token comment">//滑窗内的所有帧都和最新一帧进行视差比较</span>
vector<span class="token operator"><</span>pair<span class="token operator"><</span>Vector3d<span class="token punctuation">,</span> Vector3d<span class="token operator">>></span> corres<span class="token punctuation">;</span>
corres <span class="token operator">=</span> f_manager<span class="token punctuation">.</span><span class="token function">getCorresponding</span><span class="token punctuation">(</span>i<span class="token punctuation">,</span> WINDOW_SIZE<span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//寻找第i帧到窗口最后一帧(当前帧)的对应特征点归一化坐标</span>
<span class="token keyword">if</span> <span class="token punctuation">(</span>corres<span class="token punctuation">.</span><span class="token function">size</span><span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token operator">></span> <span class="token number">20</span><span class="token punctuation">)</span> <span class="token comment">//归一化坐标point(x,y,不需要z)</span>
<span class="token punctuation">{</span>
<span class="token keyword">double</span> sum_parallax <span class="token operator">=</span> <span class="token number">0</span><span class="token punctuation">;</span>
<span class="token keyword">double</span> average_parallax<span class="token punctuation">;</span><span class="token comment">//计算平均视差</span>
<span class="token keyword">for</span> <span class="token punctuation">(</span><span class="token keyword">int</span> j <span class="token operator">=</span> <span class="token number">0</span><span class="token punctuation">;</span> j <span class="token operator"><</span> <span class="token keyword">int</span><span class="token punctuation">(</span>corres<span class="token punctuation">.</span><span class="token function">size</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">;</span> j<span class="token operator">++</span><span class="token punctuation">)</span>
<span class="token punctuation">{</span><span class="token comment">//第j个对应点在第i帧和最后一帧的(x,y)</span>
Vector2d <span class="token function">pts_0</span><span class="token punctuation">(</span>corres<span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">.</span><span class="token function">first</span><span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">,</span> corres<span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">.</span><span class="token function">first</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//改成3，4呢(对应像素坐标，1-3是归一化xyz坐标)</span>
Vector2d <span class="token function">pts_1</span><span class="token punctuation">(</span>corres<span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">.</span><span class="token function">second</span><span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">,</span> corres<span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">.</span><span class="token function">second</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">;</span>
<span class="token keyword">double</span> parallax <span class="token operator">=</span> <span class="token punctuation">(</span>pts_0 <span class="token operator">-</span> pts_1<span class="token punctuation">)</span><span class="token punctuation">.</span><span class="token function">norm</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">;</span>
sum_parallax <span class="token operator">=</span> sum_parallax <span class="token operator">+</span> parallax<span class="token punctuation">;</span>
<span class="token punctuation">}</span><span class="token comment">//计算平均视差</span>
average_parallax <span class="token operator">=</span> <span class="token number">1.0</span> <span class="token operator">*</span> sum_parallax <span class="token operator">/</span> <span class="token keyword">int</span><span class="token punctuation">(</span>corres<span class="token punctuation">.</span><span class="token function">size</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">;</span><span class="token comment">//判断是否满足初始化条件：视差>30</span>
</code>
getCorresponding()的作用就是找到当前帧与最新一帧所有特征点在对应2帧下分别的归一化坐标，并配对，以供求出相对位姿时使用。 b.计算l帧与最新一帧的相对位姿关系
if(average_parallax * 460 > 30 && m_estimator.solveRelativeRT(corres, relative_R, relative_T))
{ //solveRelativeRT()通过基础矩阵计算当前帧与第l帧之间的R和T,并判断内点数目是否足够
l = i; //同时返回窗口最后一帧（当前帧）到第l帧（参考帧）的relative_R，relative_T
ROS_DEBUG("average_parallax %f choose l %d and newest frame to triangulate the whole structure", average_parallax * 460, l);
return true;
//一旦这一帧与当前帧视差足够大了，那么就不再继续找下去了(再找只会和当前帧的视差越来越小）
}
这里最核心的公式就是m_estimator.solveRelativeRT()，这部分非常地关键。这里面代码很简单，就是把对应的点传进入，然后套cv的公式，但是求出来的R和T是谁转向谁的比较容易迷糊。 根据对以前学习内容和回忆和对后面公式的阅读，这个relative_R和relative_T是把最新一帧旋转到第l帧的旋转平移！
bool MotionEstimator::solveRelativeRT(const vector<pair<Vector3d, Vector3d>> &corres, Matrix3d &Rotation, Vector3d &Translation)
{
if (corres.size() >= 15)
{
vector<cv::Point2f> ll, rr;
for (int i = 0; i < int(corres.size()); i++)
{
ll.push_back(cv::Point2f(corres[i].first(0), corres[i].first(1)));
rr.push_back(cv::Point2f(corres[i].second(0), corres[i].second(1)));
}
cv::Mat mask; //因为这里的ll,rr是归一化坐标，所以得到的是本质矩阵
cv::Mat E = cv::findFundamentalMat(ll, rr, cv::FM_RANSAC, 0.3 / 460, 0.99, mask);
cv::Mat cameraMatrix = (cv::Mat_<double>(3, 3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
cv::Mat rot, trans;
int inlier_cnt = cv::recoverPose(E, ll, rr, cameraMatrix, rot, trans, mask);
//cout << "inlier_cnt " << inlier_cnt << endl;
Eigen::Matrix3d R;
Eigen::Vector3d T;
for (int i = 0; i < 3; i++)
{
T(i) = trans.at<double>(i, 0);
for (int j = 0; j < 3; j++)
R(i, j) = rot.at<double>(i, j);
}
Rotation = R.transpose();
Translation = -R.transpose() * T;
if(inlier_cnt > 12)
return true;
else
return false;
}
return false;
}
/**
* Mat cv::findFundamentalMat( 返回通过RANSAC算法求解两幅图像之间的本质矩阵E
* nputArray points1, 第一幅图像点的数组
* InputArray points2, 第二幅图像点的数组
* int method = FM_RANSAC, RANSAC 算法
* double param1 = 3., 点到对极线的最大距离，超过这个值的点将被舍弃
* double param2 = 0.99, 矩阵正确的可信度
* OutputArray mask = noArray() 输出在计算过程中没有被舍弃的点
* )
*/
/**
* int cv::recoverPose ( 通过本质矩阵得到Rt，返回通过手性校验的内点个数
* InputArray E, 本质矩阵
* InputArray points1, 第一幅图像点的数组
* InputArray points2, 第二幅图像点的数组
* InputArray cameraMatrix, 相机内参
* OutputArray R, 第一帧坐标系到第二帧坐标系的旋转矩阵
* OutputArray t, 第一帧坐标系到第二帧坐标系的平移向量
* InputOutputArray mask = noArray() 在findFundamentalMat()中没有被舍弃的点
* )
*/
而且这里出现了个新的数据结构m_estimator，分析一下。 数据结构: MotionEstimator m_estimator 它定义在initial/solve_5pts.h中，这个类没有数据成员，只有函数功能。所以说，TODO 把这个h文件干掉，融到estimator.cpp里面去！
class MotionEstimator
{
public:
bool solveRelativeRT(const vector<pair<Vector3d, Vector3d>> &corres, Matrix3d &R, Vector3d &T);
private:
double testTriangulation(const vector<cv::Point2f> &l,
const vector<cv::Point2f> &r,
cv::Mat_<double> R, cv::Mat_<double> t);
void decomposeE(cv::Mat E,
cv::Mat_<double> &R1, cv::Mat_<double> &R2,
cv::Mat_<double> &t1, cv::Mat_<double> &t2);
};
这一部分的原理对应如下，
Assuming P =[X, Y, Z]T is the 3D coordinates of one feature in camera coordinate system at time bk. K is the intrinsic parameter matrix of camera. p1 and p2 are pixel coordinates of the feature on image bk and bk+1. Then it is, s1p1 = KP, s2x2 = K (RP + t). At normalized plane, the coordinates are, x1 = K-1p1, x2 = K-1p2. Then they can get, s2x2 = s1Rx1 + t. The equation left multiplies t^ which is, t^s2x2 = s1t^Rx1. Then left multiplies x2T then gets, 0 = x2T (t^R) x1, or 0 = p2T (K-Tt^RK-1) p1, c.没有满足要求的帧，整个初始化initialStructure()失败。
return false
6.3.4 construct()：对窗口中每个图像帧求解sfm问题 得到所有图像帧相对于参考帧的旋转四元数Q、平移向量T和特征点坐标(核心！) 首先，还是定义了一个容器，看一下这个容器的数据结构。
GlobalSFM sfm;
数据结构: GlobalSFM sfm
它定义在initial/initial_sfm.h中，
class GlobalSFM
{
public:
GlobalSFM();
bool construct(int frame_num, Quaterniond* q, Vector3d* T, int l,
const Matrix3d relative_R, const Vector3d relative_T,
vector<SFMFeature> &sfm_f, map<int, Vector3d> &sfm_tracked_points);
private:
bool solveFrameByPnP(Matrix3d &R_initial, Vector3d &P_initial, int i, vector<SFMFeature> &sfm_f);
void triangulatePoint(Eigen::Matrix<double, 3, 4> &Pose0, Eigen::Matrix<double, 3, 4> &Pose1 ,Vector2d &point0, Vector2d &point1, Vector3d &point_3d);
void triangulateTwoFrames(int frame0, Eigen::Matrix<double, 3, 4> &Pose0,
int frame1, Eigen::Matrix<double, 3, 4> &Pose1,
vector<SFMFeature> &sfm_f);
int feature_num;
};
所以说这个数据结构和MotionEstimator是类似的，主要是实现函数功能，他只有一个数据成员feature_num。
if(!sfm.construct(frame_count + 1, Q, T, l, //Q ck->c0?
relative_R, relative_T,
sfm_f, sfm_tracked_points))
{
ROS_DEBUG("global SFM failed!");
marginalization_flag = MARGIN_OLD;
return false;
}
这里主要干了2件事，首先SfM，传入了frame_count + 1，l, relative_R, relative_T, sfm_f这几个参数，得到了Q, T, sfm_tracked_points，这三个量的都是基于l帧上表示的！ 第二件事就是marginalization_flag = MARGIN_OLD。这说明了在初始化后期的第一次slidingwindow() marg掉的是old帧。 看一下*construct()*里面干了什么。见initial/initial_sfm.cpp文件。 (1)把第l帧作为参考坐标系，获得最新一帧在参考坐标系下的位姿
feature_num = sfm_f.size();
q[l].w() = 1; //参考帧的四元数，平移为1和0
q[l].x() = 0;
q[l].y() = 0;
q[l].z() = 0;
T[l].setZero(); //1、这里把第l帧看作参考坐标系，根据当前帧到第l帧的relative_R，relative_T，得到当前帧在参考坐标系下的位姿，之后的pose[i]表示第l帧到第i帧的变换矩阵[R|T]
q[frame_num - 1] = q[l] * Quaterniond(relative_R); //frame_num-1表示当前帧* relative c0_->ck
T[frame_num - 1] = relative_T;
(2)构造容器，存储滑窗内 第l帧 相对于 其它帧 和 最新一帧 的位姿
Matrix3d c_Rotation[frame_num];
Vector3d c_Translation[frame_num];
Quaterniond c_Quat[frame_num];
double c_rotation[frame_num][4];
double c_translation[frame_num][3];
Eigen::Matrix<double, 3, 4> Pose[frame_num];
注意，这些容器存储的都是相对运动，大写的容器对应的是l帧旋转到各个帧。 小写的容器是用于全局BA时使用的，也同样是l帧旋转到各个帧。之所以在这两个地方要保存这种相反的旋转，是因为三角化求深度的时候需要这个相反旋转的矩阵！ 为了表示区别，称这两类容器叫 坐标系变换矩阵，而不能叫 位姿 ！ (3)对于第l帧和最新一帧，它们的相对运动是已知的，可以直接放入容器
//从l帧旋转到各个帧的旋转平移
c_Quat[l] = q[l].inverse();
c_Rotation[l] = c_Quat[l].toRotationMatrix();
c_Translation[l] = -1 * (c_Rotation[l] * T[l]);
Pose[l].block<3, 3>(0, 0) = c_Rotation[l];
Pose[l].block<3, 1>(0, 3) = c_Translation[l];
c_Quat[frame_num - 1] = q[frame_num - 1].inverse();
c_Rotation[frame_num - 1] = c_Quat[frame_num - 1].toRotationMatrix();
c_Translation[frame_num - 1] = 1 * (c_Rotation[frame_num - 1] * T[frame_num - 1]);
Pose[frame_num - 1].block<3, 3>(0, 0) = c_Rotation[frame_num - 1];
Pose[frame_num - 1].block<3, 1>(0, 3) = c_Translation[frame_num - 1];
注意，这块有一个取相反旋转的操作，因为三角化的时候需要这个相反的旋转！ (4)三角化l帧和最新帧，获得他们的共视点在l帧上的空间坐标 注意，三角化的前提有1个：两帧的(相对)位姿已知。这样才能把他们的共视点的三维坐标还原出来。 triangulateTwoFrames(l, Pose[l], frame_num – 1, Pose[frame_num – 1], sfm_f); 我们看一下这个函数的内容。
void GlobalSFM::triangulateTwoFrames(int frame0, Eigen::Matrix<double, 3, 4> &Pose0,
int frame1, Eigen::Matrix<double, 3, 4> &Pose1,
vector<SFMFeature> &sfm_f)
{
assert(frame0 != frame1);
for (int j = 0; j < feature_num; j++)//在所有特征里面依次寻找
{
if (sfm_f[j].state == true)//如果这个特征已经三角化过了，那就跳过
continue;
bool has_0 = false, has_1 = false;
Vector2d point0;
Vector2d point1;
for (int k = 0; k < (int)sfm_f[j].observation.size(); k++)
{
if (sfm_f[j].observation[k].first == frame0)//如果这个特征在frame0出现过
{
point0 = sfm_f[j].observation[k].second;//把他的归一化坐标提取出来
has_0 = true;
}
if (sfm_f[j].observation[k].first == frame1)//如果这个特征在frame1出现过
{
point1 = sfm_f[j].observation[k].second;//把他的归一化坐标提取出来
has_1 = true;
}
}
if (has_0 && has_1)//如果这两个归一化坐标都存在
{
Vector3d point_3d;
triangulatePoint(Pose0, Pose1, point0, point1, point_3d);//根据他们的位姿和归一化坐标，输出在参考系l下的的空间坐标
sfm_f[j].state = true;已经完成三角化，状态更改为true
sfm_f[j].position[0] = point_3d(0);//把参考系l下的的空间坐标赋值给这个特征点的对象
sfm_f[j].position[1] = point_3d(1);
sfm_f[j].position[2] = point_3d(2);
//cout << "trangulated : " << frame1 << " 3d point : " << j << " " << point_3d.transpose() << endl;
}
}
}
这个函数的核心还是triangulatePoint(Pose0, Pose1, point0, point1, point_3d)。 首先他把sfm_f的特征点取出来，一个个地检查看看这个特征点是不是被2帧都观测到了，如果被观测到了，再执行三角化操作。那么再看看triangulatePoint()是怎么写的。
void GlobalSFM::triangulatePoint(Eigen::Matrix<double, 3, 4> &Pose0, Eigen::Matrix<double, 3, 4> &Pose1,
Vector2d &point0, Vector2d &point1, Vector3d &point_3d)
{//https://blog.csdn.net/jsf921942722/article/details/95511167
Matrix4d design_matrix = Matrix4d::Zero();
design_matrix.row(0) = point0[0] * Pose0.row(2) - Pose0.row(0);
design_matrix.row(1) = point0[1] * Pose0.row(2) - Pose0.row(1);
design_matrix.row(2) = point1[0] * Pose1.row(2) - Pose1.row(0);
design_matrix.row(3) = point1[1] * Pose1.row(2) - Pose1.row(1);
Vector4d triangulated_point;
triangulated_point =
design_matrix.jacobiSvd(Eigen::ComputeFullV).matrixV().rightCols<1>();
point_3d(0) = triangulated_point(0) / triangulated_point(3);
point_3d(1) = triangulated_point(1) / triangulated_point(3);
point_3d(2) = triangulated_point(2) / triangulated_point(3);
}
这一部分代码涉及到了三角化求深度，对应《SLAM14讲》7.5部分，对应《手写VIO》第6章。 原理如下，对于我们要求的3D坐标，可以表示成齐次形式，
世界坐标系到相机坐标系的变换矩阵，在这里，是l帧到各个帧的转换(因为所有位姿和特征点目前都是在l帧表示的)，注意，这块的表示方法和我们习惯的表示方法(一般都是相机到世界的转换)是相反的！
x为相机归一化平面坐标：
λ为深度值，已知以上条件有：
展开上式得：
对于等号右边的那个矩阵，它的第一行 ×(−u)第二行 ×(−v),再相加，即可得到第三行，因此，其线性相关，保留前两行即可，有，
因此，已知一个归一化平面坐标x和变换矩阵T可以构建两个关于X的线性方程组。有两个以上的图像观测即可求出X，
由于得到矩阵的秩大于未知量的数，所以上式方程没有非零解，使用SVD求最小二乘解，最小奇异值对应的向量即为所需的解。这个思路和IMU外参在线标定的方法很像。注意，x向量是四维了，解出来的值最后一个数需要确保它是1，所以， (5)对于在sliding window里在第l帧之后的每一帧，分别都和前一帧用PnP求它的位姿，得到位姿后再和最新一帧三角化得到它们共视点的3D坐标
for (int i = l; i < frame_num - 1 ; i++)
{
Matrix3d R_initial = c_Rotation[i - 1];
Vector3d P_initial = c_Translation[i - 1];
//已知第i帧上出现的一些特征点的l系上空间坐标，通过上一帧的旋转平移得到下一帧的旋转平移
if(!solveFrameByPnP(R_initial, P_initial, i, sfm_f))
return false;//SfM失败
c_Rotation[i] = R_initial;
c_Translation[i] = P_initial;
c_Quat[i] = c_Rotation[i];
Pose[i].block<3, 3>(0, 0) = c_Rotation[i];
Pose[i].block<3, 1>(0, 3) = c_Translation[i];
triangulateTwoFrames(i, Pose[i], frame_num - 1, Pose[frame_num - 1], sfm_f);
}
在这里，我修改了源码的结构，使得逻辑上更通畅一些。triangulateTwoFrames()之前讲过了，现在专门注意solveFrameByPnP()函数。 PnP在《SLAM14讲》的7.7部分，一般来讲，求位姿，2D-2D对极几何只是在第一次使用，也就是没有3D特征点坐标的时候使用，一旦有了特征点，之后都会用3D-2D的方式求位姿。然后会进入PnP求新位姿，然后三角化求新3D坐标的循环中。 solveFrameByPnP()代码逻辑可以分成4部分， a．第一次筛选：把滑窗的所有特征点中，那些没有3D坐标的点pass掉。
bool GlobalSFM::solveFrameByPnP(Matrix3d &R_initial, Vector3d &P_initial, int i, vector<SFMFeature> &sfm_f)
{
vector<cv::Point2f> pts_2_vector;
vector<cv::Point3f> pts_3_vector;
for (int j = 0; j < feature_num; j++)//feature_num = sfm_f.size() line121
{//要把待求帧i上所有特征点的归一化坐标和3D坐标(l系上)都找出来
if (sfm_f[j].state != true)//这个特征点没有被三角化为空间点，跳过这个点的PnP
continue;
b．因为是对当前帧和上一帧进行PnP，所以这些有3D坐标的特征点，不仅得在当前帧被观测到，还得在上一帧被观测到。
Vector2d point2d;
for (int k=0; k < (int)sfm_f[j].observation.size(); k++)//依次遍历特征j在每一帧中的归一化坐标
{
if (sfm_f[j].observation[k].first == i)//如果该特征在帧i上出现过
{
Vector2d img_pts = sfm_f[j].observation[k].second;
cv::Point2f pts_2(img_pts(0), img_pts(1));
pts_2_vector.push_back(pts_2);//把在待求帧i上出现过的特征的归一化坐标放到容器中
cv::Point3f pts_3(sfm_f[j].position[0], sfm_f[j].position[1], sfm_f[j].position[2]);
pts_3_vector.push_back(pts_3);//把在待求帧i上出现过的特征在参考系l的空间坐标放到容器中
break;//因为一个特征在帧i上只会出现一次，一旦找到了就没有必要再继续找了
}
}
}
c.如果这些有3D坐标的特征点，并且在当前帧和上一帧都出现了，数量却少于15，那么整个初始化全部失败。因为它的是层层往上传递。
if (int(pts_2_vector.size()) < 15)
{
printf("unstable features tracking, please slowly move you device!\n");
if (int(pts_2_vector.size()) < 10)
return false;
}
d．套用openCV的公式，进行PnP求解。
cv::Mat r, rvec, t, D, tmp_r;
cv::eigen2cv(R_initial, tmp_r);//转换成solvePnP能处理的格式
cv::Rodrigues(tmp_r, rvec);
cv::eigen2cv(P_initial, t);
cv::Mat K = (cv::Mat_<double>(3, 3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
bool pnp_succ;
pnp_succ = cv::solvePnP(pts_3_vector, pts_2_vector, K, D, rvec, t, 1);//得到了第i帧到第l帧的旋转平移
if(!pnp_succ)
{
return false;
}
cv::Rodrigues(rvec, r);
//cout << "r " << endl << r << endl;
MatrixXd R_pnp;
cv::cv2eigen(r, R_pnp);//转换成原有格式
MatrixXd T_pnp;
cv::cv2eigen(t, T_pnp);
R_initial = R_pnp;//覆盖原先的旋转平移
P_initial = T_pnp;
return true;
}
(6)从第l+1帧到滑窗的最后的每一帧再与第l帧进行三角化补充3D坐标 现在回到construct()函数，在上一步，求出了l帧后面的每一帧的位姿，也求出了它们相对于最后一帧的共视点的3D坐标，但是这是不够的，现在继续补充3D坐标，那么就和第l帧进行三角化。
for (int i = l + 1; i < frame_num - 1; i++)
triangulateTwoFrames(l, Pose[l], i, Pose[i], sfm_f);
(7)对于在sliding window里在第l帧之前的每一帧，分别都和后一帧用PnP求它的位姿，得到位姿后再和第l帧三角化得到它们共视点的3D坐标
for (int i = l - 1; i >= 0; i--)
{
//solve pnp
Matrix3d R_initial = c_Rotation[i + 1];
Vector3d P_initial = c_Translation[i + 1];
if(!solveFrameByPnP(R_initial, P_initial, i, sfm_f))
return false;
c_Rotation[i] = R_initial;
c_Translation[i] = P_initial;
c_Quat[i] = c_Rotation[i];
Pose[i].block<3, 3>(0, 0) = c_Rotation[i];
Pose[i].block<3, 1>(0, 3) = c_Translation[i];
//triangulate
triangulateTwoFrames(i, Pose[i], l, Pose[l], sfm_f);
}
l帧之后的帧都有着落了，现在解决之前的帧。这个过程和(5)完全一样。 (8) 三角化其他未恢复的特征点 至此得到了滑动窗口中所有图像帧的位姿以及特征点的3D坐标。
for (int j = 0; j < feature_num; j++)
{
if (sfm_f[j].state == true)//estimator.cpp line258 是否已经三角化了
continue;
if ((int)sfm_f[j].observation.size() >= 2)//里面存放着第j个特征在滑窗所有帧里的归一化坐标
{
Vector2d point0, point1;
int frame_0 = sfm_f[j].observation[0].first;//第j个特征在滑窗第一次被观测到的帧的ID
point0 = sfm_f[j].observation[0].second;//第j个特征在滑窗第一次被观测到的帧的归一化坐标
int frame_1 = sfm_f[j].observation.back().first;//第j个特征在滑窗最后一次被观测到的帧的ID
point1 = sfm_f[j].observation.back().second;//第j特征在滑窗最后一次被观测到的帧的归一化坐标
Vector3d point_3d;//在帧l下的空间坐标
triangulatePoint(Pose[frame_0], Pose[frame_1], point0, point1, point_3d);
sfm_f[j].state = true;
sfm_f[j].position[0] = point_3d(0);
sfm_f[j].position[1] = point_3d(1);
sfm_f[j].position[2] = point_3d(2);
}
}
(9)采用ceres进行全局BA a.声明problem 注意，因为四元数是四维的，但是自由度是3维的，因此需要引入LocalParameterization。
ceres::Problem problem;
ceres::LocalParameterization* local_parameterization = new ceres::QuaternionParameterization();//解决
b.加入待优化量：全局位姿
for (int i = 0; i < frame_num; i++)//7、使用cares进行全局BA优化
{
//double array for ceres
c_translation[i][0] = c_Translation[i].x();
c_translation[i][1] = c_Translation[i].y();
c_translation[i][2] = c_Translation[i].z();
c_rotation[i][0] = c_Quat[i].w();
c_rotation[i][1] = c_Quat[i].x();
c_rotation[i][2] = c_Quat[i].y();
c_rotation[i][3] = c_Quat[i].z();
problem.AddParameterBlock(c_rotation[i], 4, local_parameterization);
problem.AddParameterBlock(c_translation[i], 3);//value,size
在这里，可以发现，仅仅是位姿被优化了，特征点的3D坐标没有被优化！ c.固定先验值 因为l帧是参考系，最新帧的平移也是先验，如果不固定住，原本可观的量会变的不可观。
if (i == l)
{
problem.SetParameterBlockConstant(c_rotation[i]);
}
if (i == l || i == frame_num - 1)
{
problem.SetParameterBlockConstant(c_translation[i]);
}
}
d.加入残差块 这里采用的仍然是最小化重投影误差的方式，所以需要2D-3D信息，注意这块没有加loss function。
for (int i = 0; i < feature_num; i++)
{
if (sfm_f[i].state != true)
continue;
for (int j = 0; j < int(sfm_f[i].observation.size()); j++)
{
int l = sfm_f[i].observation[j].first;
ceres::CostFunction* cost_function = ReprojectionError3D::Create(
sfm_f[i].observation[j].second.x(),
sfm_f[i].observation[j].second.y());
problem.AddResidualBlock(cost_function, NULL, c_rotation[l], c_translation[l],
sfm_f[i].position);
}
}
e.shur消元求解
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_SCHUR;
options.max_solver_time_in_seconds = 0.2;
ceres::Solver::Summary summary;
ceres::Solve(options, &problem, &summary);
if (!(summary.termination_type == ceres::CONVERGENCE && summary.final_cost < 5e-03))
{
return false;
}
这块出现了shur消元知识点，复习一下。shur消元有2大作用，一个是在最小二乘中利用H矩阵稀疏的性质进行加速求解，另一个是在sliding window时求解marg掉老帧后的先验信息矩阵。这块是shur消元的第一个用法。 d．返回特征点l系下3D坐标和优化后的全局位姿
for (int i = 0; i < frame_num; i++)
{
q[i].w() = c_rotation[i][0];
q[i].x() = c_rotation[i][1];
q[i].y() = c_rotation[i][2];
q[i].z() = c_rotation[i][3];
q[i] = q[i].inverse();
}
for (int i = 0; i < frame_num; i++)
{
T[i] = -1 * (q[i] * Vector3d(c_translation[i][0], c_translation[i][1], c_translation[i][2]));
}
for (int i = 0; i < (int)sfm_f.size(); i++)
{
if(sfm_f[i].state)
sfm_tracked_points[sfm_f[i].id] = Vector3d(sfm_f[i].position[0], sfm_f[i].position[1], sfm_f[i].position[2]);
}
return true;
}
优化完成后，需要获得各帧在帧l系下的位姿(也就是各帧到l帧的旋转平移)，所以需要inverse操作，然后把特征点在帧l系下的3D坐标传递出来。 至此，construct()函数全部完成！ 6.3.5 给滑窗外的图像帧提供初始的RT估计，然后solvePnP进行优化
目的：求出所有帧对应的IMU坐标系bk到l帧的旋转和ck到l帧的平移，和论文中初始化部分头两个公式对应上！
现在再次回到estimator.cpp文件，看下一步流程。在之前的流程里，求出了滑窗内所有帧和对应特征点在l帧下的状态，现在要求出所有帧，也就是滑窗外的，也求解出来。 这部分代码最外层有一个for循环，也就是遍历所有的图像帧：
map<double, ImageFrame>::iterator frame_it;
map<int, Vector3d>::iterator it;
frame_it = all_image_frame.begin( );
for (int i = 0; frame_it != all_image_frame.end( ); frame_it++)
{
// provide initial guess
cv::Mat r, rvec, t, D, tmp_r;
//对当前图像帧的操作
...
}
TODO 我感到费解的是，这部分为什么没有对i<WINDOW_SIZE+1这个判断呢？ a.边界判断：对于滑窗内的帧，把它们设为关键帧，并获得它们对应的IMU坐标系到l系的旋转平移
if((frame_it->first) == Headers[i].stamp.toSec())
{
frame_it->second.is_key_frame = true;
frame_it->second.R = Q[i].toRotationMatrix() * RIC[0].transpose();
frame_it->second.T = T[i];
i++;
continue;
}
这部分代码虽然看起来简单，实际上有点绕，涉及到的数据结构很多。这里，要注意一下，Headers，Q和T，它们的size都是WINDOW_SIZE+1！它们存储的信息都是滑窗内的，尤其是Q和T，它们都是当前视觉帧到l帧(也是视觉帧)系到旋转平移。 所以一开始，通过时间戳判断是不是滑窗内的帧； 如果是，那么设置为关键帧； 接下来的两行，非常重要，注意一下ImageFrame这个数据结构，见5.2-2，它不仅包括了图像信息，还包括了对应的IMU的位姿信息和IMU预积分信息，而这里，是这些帧第一次获得它们对应的IMU的位姿信息的位置！也就是bk->l帧的旋转平移！ b．边界判断：如果当前帧的时间戳大于滑窗内第i帧的时间戳，那么i++
//TODO delete this judgement
if((frame_it->first) > Headers[i].stamp.toSec())
i++; //TODO change to ++i
代码好懂，但是不明白为什么这样写？这一部分有点像kmp算法中的2把尺子，一把大一点的尺子是all_image_frame，另一把小尺子是Headers，分别对应着所有帧的长度和滑窗长度； 小尺子固定，大尺子在上面滑动； 每次循环，大尺子滑动一格； 因为小尺子比较靠后，所以开始的时候只有大尺子在动，小尺子不动； 如果大尺子和小尺子刻度一样的时候，小尺子也走一格； 如果大尺子的刻度比小尺子大，小尺子走一格； 但是问题是，我觉得小尺子的刻度，对应在大尺子的最后面！TODO把这一部分注释掉，看看效果！ c.对滑窗外的所有帧，求出它们对应的IMU坐标系到l帧的旋转平移
//注意这里的 Q和 T是图像帧的位姿，而不是求解PNP时所用的坐标系变换矩阵，两者具有对称关系
Matrix3d R_inital = (Q[i].inverse()).toRotationMatrix();
Vector3d P_inital = - R_inital * T[i];
cv::eigen2cv(R_inital, tmp_r);
cv::Rodrigues(tmp_r, rvec);//罗德里格斯公式将旋转矩阵转换成旋转向量
cv::eigen2cv(P_inital, t);
frame_it->second.is_key_frame = false;
vector<cv::Point3f> pts_3_vector;//获取 pnp需要用到的存储每个特征点三维点和图像坐标的 vector
vector<cv::Point2f> pts_2_vector;
for (auto &id_pts : frame_it->second.points)
{
int feature_id = id_pts.first;
for (auto &i_p : id_pts.second)
{
it = sfm_tracked_points.find(feature_id);
if(it != sfm_tracked_points.end())
{
Vector3d world_pts = it->second;
cv::Point3f pts_3(world_pts(0), world_pts(1), world_pts(2));
pts_3_vector.push_back(pts_3);
Vector2d img_pts = i_p.second.head<2>();
cv::Point2f pts_2(img_pts(0), img_pts(1));
pts_2_vector.push_back(pts_2);
}
}
}//保证特征点数大于 5
cv::Mat K = (cv::Mat_<double>(3, 3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
if(pts_3_vector.size() < 6)
{
cout << "pts_3_vector size " << pts_3_vector.size() << endl;
ROS_DEBUG("Not enough points for solve pnp !");
return false;
}
if (! cv::solvePnP(pts_3_vector, pts_2_vector, K, D, rvec, t, 1))
{
ROS_DEBUG("solve pnp fail!");
return false;
}
cv::Rodrigues(rvec, r);
MatrixXd R_pnp,tmp_R_pnp;
cv::cv2eigen(r, tmp_R_pnp);
R_pnp = tmp_R_pnp.transpose();
MatrixXd T_pnp;
cv::cv2eigen(t, T_pnp);
T_pnp = R_pnp * (-T_pnp);
frame_it->second.R = R_pnp * RIC[0].transpose();
frame_it->second.T = T_pnp;
}
这部分和之前讲的的部分思路一样，唯一需要注意的是最后两行，这块明显是告诉我们传入的是bk->l的旋转平移。 6.3.6 visualInitialAlign() (核心！)
if (visualInitialAlign())
return true;
else
return false;
}
6.4 visualInitialAlign()
基本上，初始化的理论部分都在visualInitialAlign()函数里。 6.4.1计算陀螺仪偏置，尺度，重力加速度和速度
TicToc t_g;
VectorXd x;
if(!VisualIMUAlignment(all_image_frame, Bgs, g, x))
return false;
进去看一下VisualIMUAlignment()的具体内容，它在initial/initial_alignment.h里面。
bool VisualIMUAlignment(map<double, ImageFrame> &all_image_frame, Vector3d* Bgs, Vector3d &g, VectorXd &x)
{
solveGyroscopeBias(all_image_frame, Bgs);//陀螺仪的偏置进行标定
if(LinearAlignment(all_image_frame, g, x))//估计尺度、重力以及速度
return true;
else
return false;
}
solveGyroscopeBias()对应在6.1.3部分。 LinearAlignment()对应在6.1.4和6.1.5部分。 6.4.2 传递所有图像帧的位姿Ps、Rs，并将其置为关键帧
for (int i = 0; i <= frame_count; i++)
{
Matrix3d Ri = all_image_frame[Headers[i].stamp.toSec()].R;
Vector3d Pi = all_image_frame[Headers[i].stamp.toSec()].T;
Ps[i] = Pi;
Rs[i] = Ri;
all_image_frame[Headers[i].stamp.toSec()].is_key_frame = true;
}
6.4.3重新计算所有f_manager的特征点深度
VectorXd dep = f_manager.getDepthVector();
for (int i = 0; i < dep.size(); i++)
dep[i] = -1;//将所有特征点的深度置为-1
f_manager.clearDepth(dep);
//triangulat on cam pose , no tic //重新计算特征点的深度
Vector3d TIC_TMP[NUM_OF_CAM];
for(int i = 0; i < NUM_OF_CAM; i++)
TIC_TMP[i].setZero();
ric[0] = RIC[0];
f_manager.setRic(ric);
f_manager.triangulate(Ps, &(TIC_TMP[0]), &(RIC[0]));
这里有2个ric，小ric是vins自己计算得到的，大RIC是从yaml读取的，我觉得没什么区别。 这里需要注意一下，这个triangulate()和之前出现的并不是同一个函数。 它是定义在feature_manager.cpp里的，之前的是在initial_sfm.cpp里面。它们服务的对象是不同的数据结构！ 6.4.4 IMU的bias改变，重新计算滑窗内的预积分
for (int i = 0; i <= WINDOW_SIZE; i++)
{
pre_integrations[i]->repropagate(Vector3d::Zero(), Bgs[i]);
}
TODO 我觉得这块操作在6.4.1的solveGyroscopeBias()部分重复了，注释掉看看 现在又想了一下，感觉并不是重复了，因为6.4.1是更新完角速度bias后重新算了一下提升了精度，现在再算一下，又提升了一次精度，就算是注释掉，也是注释掉6.4.1里面的那个？试一下。 6.4.5 将Ps、Vs、depth尺度s缩放后从l帧转变为相对于c0帧图像坐标系下
double s = (x.tail<1>())(0);
for (int i = frame_count; i >= 0; i--)
Ps[i] = s * Ps[i] - Rs[i] * TIC[0] - (s * Ps[0] - Rs[0] * TIC[0]);
int kv = -1;
map<double, ImageFrame>::iterator frame_i;
for (frame_i = all_image_frame.begin(); frame_i != all_image_frame.end(); frame_i++)
{
if(frame_i->second.is_key_frame)
{
kv++;
Vs[kv] = frame_i->second.R * x.segment<3>(kv * 3);
}
}
for (auto &it_per_id : f_manager.feature)
{
it_per_id.used_num = it_per_id.feature_per_frame.size();
if (!(it_per_id.used_num >= 2 && it_per_id.start_frame < WINDOW_SIZE - 2))
continue;
it_per_id.estimated_depth *= s;
}
6.4.6 通过将重力旋转到z轴上，得到世界坐标系与摄像机坐标系c0之间的旋转矩阵rot_diff
Matrix3d R0 = Utility::g2R(g);
double yaw = Utility::R2ypr(R0 * Rs[0]).x();
R0 = Utility::ypr2R(Eigen::Vector3d{-yaw, 0, 0}) * R0;
g = R0 * g;
//Matrix3d rot_diff = R0 * Rs[0].transpose();
Matrix3d rot_diff = R0;
6.4.7 所有变量从参考坐标系c0旋转到世界坐标系w
for (int i = 0; i <= frame_count; i++)
{
Ps[i] = rot_diff * Ps[i];
Rs[i] = rot_diff * Rs[i];
Vs[i] = rot_diff * Vs[i];
}
return true;
}
至此，初始化的工作全部完成！！ 代码量巨长，一半的工作在于视觉SfM(这部分作用仅仅负责求相机pose)，另一半才是论文里说的松耦合初始化！在initialStructure()的前半部分里，新建了一组sfm_f这样一批特征点，虽然和f_manager有数据重复，但它们的作用仅仅是用来配合求pose的，而且这个数据结构是建在栈区的，函数结束后统统销毁。而且找l帧这个操作很巧妙。 后半部分，就是论文里的内容。见初始化(理论部分)。
开心洋葱 , 版权所有丨如未注明 , 均为原创丨未经授权请勿修改 , 转载请注明【SLAM】VINS-MONO解析——初始化(代码部分)！
