这一讲包含了一个用g2o库进行曲线拟合的实例，但是在按照书中实际步骤实际运行发现了几个问题。
（1）g2o库的的依赖项安装
书中所写命令如下：
sudo apt-get install libqt4-dev qt4-qmake libqglviewer-dev libsuitesparse-dev libcxsparse3.1.2 libcholmod-dev
但是最后一项（libcholmod-dev）会提示不能安装，此时应该用tab键进行补全完成安装
（2）编译出错
在使用cmake编译中，出现了以下报错：
1 By not providing "FindG2O.cmake" in CMAKE_MODULE_PATH this project has 2 asked CMake to find a package configuration file provided by "G2O", but 3 CMake did not find one. 4 5 Could not find a package configuration file provided by "G2O" with any of 6 the following names: 7 8 G2OConfig.cmake 9 g2o-config.cmake 10 11 Add the installation prefix of "G2O" to CMAKE_PREFIX_PATH or set "G2O_DIR" 12 to a directory containing one of the above files. If "G2O" provides a 13 separate development package or SDK, be sure it has been installed.
原因在于CMakeLists.txt中，虽然定义了CMAKE_MODULE_PATH，但没有对应的文件和文件夹，也就是没有找到FindG2O.cmake文件
解决办法：找到findg2o.cmake文件的位置，一般在g2o安装包中的"cmake_modules"文件夹中，将整个文件夹复制到相应工程的下面，与build文件夹在同一目录，然后编译可以正常通过
*（3）CMakeLists.txt文件问题
高博在GitHub中给出的代码如下:
1 cmake_minimum_required( VERSION 2.8 )
2 project( g2o_curve_fitting )
3
4 set( CMAKE_BUILD_TYPE "Release" )
5 set( CMAKE_CXX_FLAGS "-std=c++11 -O3" )
6
7 # 添加cmake模块以使用ceres库
8 list( APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules )
9
10 # 寻找G2O
11 find_package( G2O REQUIRED )
12 include_directories(
13 ${G2O_INCLUDE_DIRS}
14 "/usr/include/eigen3"
15 )
16
17 # OpenCV
18 find_package( OpenCV REQUIRED )
19 include_directories( ${OpenCV_DIRS} )
20
21 add_executable( curve_fitting main.cpp )
22 # 与G2O和OpenCV链接
23 target_link_libraries( curve_fitting
24 ${OpenCV_LIBS}
25 g2o_core g2o_stuff
26 )
由于项目名定的是g2o_curve_fitting，因此需要修改最后的target_link_libraries（g2o_curve_fitting ${OpenCV_LIBS} g2o_core g2o_stuff）
（这个只是我遇见的问题，如果还有其他的问题，欢迎一起交流讨论）
最后整个代码的运行结果如下
generating data: 0 2.71828 0.01 2.93161 0.02 2.12942 0.03 2.46037 0.04 4.18814 0.05 2.73368 0.06 2.42751 0.07 3.44729 0.08 3.72543 0.09 2.1358 0.1 4.12333 0.11 3.38199 0.12 4.81164 0.13 1.62582 0.14 1.76862 0.15 3.21555 0.16 3.0922 0.17 5.82752 0.18 4.29855 0.19 2.74081 0.2 5.75724 0.21 3.53729 0.22 1.95514 0.23 2.99195 0.24 3.28739 0.25 4.70749 0.26 6.24365 0.27 5.81645 0.28 4.88402 0.29 4.75991 0.3 7.25246 0.31 5.92933 0.32 7.00306 0.33 5.22286 0.34 5.16179 0.35 7.26191 0.36 6.40545 0.37 6.25549 0.38 6.56094 0.39 6.53523 0.4 8.14891 0.41 7.77616 0.42 7.40141 0.43 8.75638 0.44 7.20606 0.45 7.57795 0.46 8.21564 0.47 9.84032 0.48 6.96725 0.49 9.90619 0.5 9.27125 0.51 9.87567 0.52 10.3412 0.53 9.55315 0.54 11.3635 0.55 10.8815 0.56 13.0648 0.57 11.4756 0.58 11.337 0.59 13.2393 0.6 13.5299 0.61 14.0441 0.62 13.31 0.63 13.672 0.64 14.8504 0.65 14.2599 0.66 14.7724 0.67 17.4339 0.68 17.4632 0.69 17.7598 0.7 16.8223 0.71 19.9468 0.72 20.5446 0.73 21.3767 0.74 20.1435 0.75 20.3088 0.76 23.2543 0.77 23.4349 0.78 22.8706 0.79 24.094 0.8 25.4183 0.81 25.5237 0.82 27.9738 0.83 28.5861 0.84 29.5703 0.85 29.6744 0.86 32.667 0.87 34.2698 0.88 33.5124 0.89 36.1479 0.9 39.2485 0.91 40.988 0.92 41.5716 0.93 41.3686 0.94 44.285 0.95 42.8312 0.96 47.7941 0.97 48.5931 0.98 51.8487 0.99 51.0258 start optimization iteration= 0 chi2= 30373.727656 time= 0.000163358 cumTime= 0.000163358 edges= 100 schur= 0 lambda= 699.050482 levenbergIter= 7 iteration= 1 chi2= 13336.948288 time= 5.5717e-05 cumTime= 0.000219075 edges= 100 schur= 0 lambda= 1864.134619 levenbergIter= 3 iteration= 2 chi2= 6946.262996 time= 3.5332e-05 cumTime= 0.000254407 edges= 100 schur= 0 lambda= 1242.756412 levenbergIter= 1 iteration= 3 chi2= 271.023166 time= 3.5225e-05 cumTime= 0.000289632 edges= 100 schur= 0 lambda= 414.252137 levenbergIter= 1 iteration= 4 chi2= 118.903887 time= 4.2411e-05 cumTime= 0.000332043 edges= 100 schur= 0 lambda= 138.084046 levenbergIter= 1 iteration= 5 chi2= 113.568660 time= 3.5035e-05 cumTime= 0.000367078 edges= 100 schur= 0 lambda= 46.028015 levenbergIter= 1 iteration= 6 chi2= 107.476457 time= 4.2275e-05 cumTime= 0.000409353 edges= 100 schur= 0 lambda= 15.342672 levenbergIter= 1 iteration= 7 chi2= 103.014522 time= 3.5151e-05 cumTime= 0.000444504 edges= 100 schur= 0 lambda= 5.114224 levenbergIter= 1 iteration= 8 chi2= 101.988348 time= 4.9545e-05 cumTime= 0.000494049 edges= 100 schur= 0 lambda= 1.704741 levenbergIter= 1 iteration= 9 chi2= 101.937388 time= 4.9565e-05 cumTime= 0.000543614 edges= 100 schur= 0 lambda= 0.568247 levenbergIter= 1 iteration= 10 chi2= 101.937021 time= 2.6884e-05 cumTime= 0.000570498 edges= 100 schur= 0 lambda= 0.378831 levenbergIter= 1 iteration= 11 chi2= 101.937020 time= 3.4683e-05 cumTime= 0.000605181 edges= 100 schur= 0 lambda= 0.252554 levenbergIter= 1 iteration= 12 chi2= 101.937020 time= 3.5072e-05 cumTime= 0.000640253 edges= 100 schur= 0 lambda= 0.168370 levenbergIter= 1 iteration= 13 chi2= 101.937020 time= 6.239e-05 cumTime= 0.000702643 edges= 100 schur= 0 lambda= 3678.088107 levenbergIter= 6 iteration= 14 chi2= 101.937020 time= 5.4525e-05 cumTime= 0.000757168 edges= 100 schur= 0 lambda= 19616.469906 levenbergIter= 3 iteration= 15 chi2= 101.937020 time= 4.9616e-05 cumTime= 0.000806784 edges= 100 schur= 0 lambda= 836969.382664 levenbergIter= 4 iteration= 16 chi2= 101.937020 time= 5.7449e-05 cumTime= 0.000864233 edges= 100 schur= 0 lambda= 224672257893341.656250 levenbergIter= 7 solve time cost = 0.0013663 seconds. estimated model: 0.890911 2.1719 0.943629
原文地址：https://www.cnblogs.com/qi-zhang/p/9365199.html
时间： 2024-10-27 22:16:48