关于关于高博3d2d程序报错的改动（摘）

摘自：http://blog.csdn.net/robinhjwy/article/details/78084210

想直接改动，在《还是g2o初始化一些》那篇，这篇比较啰嗦，主要是记录自己思考的步骤。 
首先说明主题：没文化真可怕 
好了，说干货。之前高博的代码。只要涉及g2o的部分，一律跑不通，没重装之前，只是报一个段错误，就没了。也排查不出来问题。 
今日2017年9月24日重装系统，重新搭建环境，除了opencv外，其他一律重新下载安装的。g2o从github下载安装的。

先贴一下g2o初始化的代码：

// 初始化g2o    typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block;  // pose 维度为 6, landmark 维度为 3    Block::LinearSolverType* linearSolver = new g2o::LinearSolverCSparse<Block::PoseMatrixType>(); // 线性方程求解器    //std::unique_ptr<Block::LinearSolverType> linearSolver ( new g2o::LinearSolverCSparse<Block::PoseMatrixType>());    Block* solver_ptr = new Block ( linearSolver );    //std::unique_ptr<Block> solver_ptr ( new Block ( std::move(linearSolver)));     // 矩阵块求解器    g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr);    //g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( std::move(solver_ptr));    g2o::SparseOptimizer optimizer;    optimizer.setAlgorithm ( solver );
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安装好之后，运行第七章的3d2d程序，还是跑不通： 
报错：

/home/robin/CLionProjects/slambook-robin/ch7/pose_estimation_3d2d/main.cpp:217:50: error: no matching function for call to ‘g2o::BlockSolver<g2o::BlockSolverTraits<6, 3> >::BlockSolver(g2o::BlockSolver<g2o::BlockSolverTraits<6, 3> >::LinearSolverType*&)’     Block* solver_ptr = new Block ( linearSolver );
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点开位置发现是g2o初始化中的Block构造的时候报错：

Block* solver_ptr = new Block ( linearSolver );
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继续点下面的错误信息：

In file included from /usr/local/include/g2o/core/block_solver.h:199:0,                 from /home/robin/CLionProjects/slambook-robin/ch7/pose_estimation_3d2d/main.cpp:11:/usr/local/include/g2o/core/block_solver.hpp:40:1: note: candidate: g2o::BlockSolver<Traits>::BlockSolver(std::unique_ptr<typename Traits::LinearSolverType>) [with Traits = g2o::BlockSolverTraits<6, 3>; typename Traits::LinearSolverType = g2o::LinearSolver<Eigen::Matrix<double, 6, 6, 0> >] BlockSolver<Traits>::BlockSolver(std::unique_ptr<LinearSolverType> linearSolver)
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发现跳转到了block_solver.hpp中BlockSolver的构造函数去了，发现是这样写的：

BlockSolver<Traits>::BlockSolver(std::unique_ptr<LinearSolverType> linearSolver)    :   BlockSolverBase(),        _linearSolver(std::move(linearSolver))
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发现BlockSolver构造时需要的是std::unique_ptr类型的linearSolver参数，这里还知道是啥玩意儿，不过看见ptr猜测是个指针。对比程序中上面写的，发现是这样的：

Block::LinearSolverType* linearSolver
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猜测是不是因为std::unique_ptr与Block::LinearSolverType*类型不一样导致的？ 
但是高博写的应该没有错误，是不是g2o更新导致用法不一样了？看一下高博书中自带的g2o的代码，发现果然不一样： 
高博书中的g2o是这么写的：

BlockSolver<Traits>::BlockSolver(LinearSolverType* linearSolver) :  BlockSolverBase(),  _linearSolver(linearSolver)
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好，找到了问题原因，下面修改： 
百度一发unique_ptr用法，这里是个网址： 
http://blog.csdn.net/pi9nc/article/details/12227887

参考着进行改动：

// 初始化g2o    typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block;  // pose 维度为 6, landmark 维度为 3    //Block::LinearSolverType* linearSolver = new g2o::LinearSolverCSparse<Block::PoseMatrixType>(); // 线性方程求解器    std::unique_ptr<Block::LinearSolverType> linearSolver ( new g2o::LinearSolverCSparse<Block::PoseMatrixType>());    //Block* solver_ptr = new Block ( linearSolver );    std::unique_ptr<Block> solver_ptr ( new Block ( linearSolver));    //std::unique_ptr<Block> solver_ptr ( new Block ( std::move(linearSolver)));     // 矩阵块求解器    g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr);    //g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( std::move(solver_ptr));    g2o::SparseOptimizer optimizer;    optimizer.setAlgorithm ( solver );
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把linearSolver和solver_ptr都改成了std::unique_ptr<>类型，并且初始化时不再用等号。 
运行，发现还是报错：

/home/robin/CLionProjects/slambook-robin/ch7/pose_estimation_3d2d/main.cpp:218:65: error: use of deleted function ‘std::unique_ptr<_Tp, _Dp>::unique_ptr(const std::unique_ptr<_Tp, _Dp>&) [with _Tp = g2o::LinearSolver<Eigen::Matrix<double, 6, 6, 0> >; _Dp = std::default_delete<g2o::LinearSolver<Eigen::Matrix<double, 6, 6, 0> > >]’     std::unique_ptr<Block> solver_ptr ( new Block ( linearSolver));
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在这一句： 
std::unique_ptr<Block> solver_ptr ( new Block ( linearSolver)); 
并且后面的一句：

g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr);
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也是报错。

第一句的：

std::unique_ptr<Block::LinearSolverType> linearSolver ( new g2o::LinearSolverCSparse<Block::PoseMatrixType>());
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并没有报错，说明这一句是对的！ 
观察报错两句，发现都存在将unique_ptr作为参数传递给类的构造函数中的用法，然后那篇博客中有个move指令，猜测是不是直接传递不行，要用move才对，继续修改：

// 初始化g2o    typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block;  // pose 维度为 6, landmark 维度为 3    //Block::LinearSolverType* linearSolver = new g2o::LinearSolverCSparse<Block::PoseMatrixType>(); // 线性方程求解器    std::unique_ptr<Block::LinearSolverType> linearSolver ( new g2o::LinearSolverCSparse<Block::PoseMatrixType>());    //Block* solver_ptr = new Block ( linearSolver );    //std::unique_ptr<Block> solver_ptr ( new Block ( linearSolver));    std::unique_ptr<Block> solver_ptr ( new Block ( std::move(linearSolver)));     // 矩阵块求解器    //g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr);    g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( std::move(solver_ptr));    g2o::SparseOptimizer optimizer;    optimizer.setAlgorithm ( solver );
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在传递linearSolver和solver_ptr时，加上了std::move()。再试一发！ 
OK，没毛病了，在得出RT之后调用BA，优化结果输出，并且退出时没有报错！ 
运行结果：

R=[0.9978662026232452, -0.05167241669574823, 0.03991244185821538; 0.0505958920800001, 0.9983397626650758, 0.02752769489929838; -0.04126860025362437, -0.02544955077522656, 0.9988239199170421]t=[-0.6361298163857759; -0.03753650998182134; 0.3069292389205321]calling bundle adjustmentiteration= 0     chi2= 1.040183  time= 0.00270625    cumTime= 0.00270625     edges= 76   schur= 1    lambda= 78.843278   levenbergIter= 1iteration= 1     chi2= 0.000789  time= 0.00202973    cumTime= 0.00473598     edges= 76   schur= 1    lambda= 26.281093   levenbergIter= 1iteration= 2     chi2= 0.000000  time= 0.00200341    cumTime= 0.00673939     edges= 76   schur= 1    lambda= 17.520729   levenbergIter= 1iteration= 3     chi2= 0.000000  time= 0.00202996    cumTime= 0.00876935     edges= 76   schur= 1    lambda= 11.680486   levenbergIter= 1iteration= 4     chi2= 0.000000  time= 0.00200547    cumTime= 0.0107748  edges= 76   schur= 1    lambda= 7.786990    levenbergIter= 1iteration= 5     chi2= 0.000000  time= 0.00200838    cumTime= 0.0127832  edges= 76   schur= 1    lambda= 5.191327    levenbergIter= 1iteration= 6     chi2= 0.000000  time= 0.00200928    cumTime= 0.0147925  edges= 76   schur= 1    lambda= 3.460885    levenbergIter= 1iteration= 7     chi2= 0.000000  time= 0.00202289    cumTime= 0.0168154  edges= 76   schur= 1    lambda= 2.307256    levenbergIter= 1iteration= 8     chi2= 0.000000  time= 0.00197821    cumTime= 0.0187936  edges= 76   schur= 1    lambda= 1.538171    levenbergIter= 1iteration= 9     chi2= 0.000000  time= 0.00202251    cumTime= 0.0208161  edges= 76   schur= 1    lambda= 1.025447    levenbergIter= 1iteration= 10    chi2= 0.000000  time= 0.00206846    cumTime= 0.0228846  edges= 76   schur= 1    lambda= 0.683632    levenbergIter= 1iteration= 11    chi2= 0.000000  time= 0.00207496    cumTime= 0.0249595  edges= 76   schur= 1    lambda= 0.455754    levenbergIter= 1iteration= 12    chi2= 0.000000  time= 0.0133918     cumTime= 0.0383513  edges= 76   schur= 1    lambda= 122340628.619249    levenbergIter= 7after optimization:T=  0.997777 -0.0521927  0.0414303  -0.647023 0.0510161   0.998278  0.0289658 -0.0492099-0.0428708 -0.0267878   0.998721     0.3004         0          0          0          1Process finished with exit code 0