前言
ceres-solver 和 g2o 是slam领域常见的优化器，其中ceres-solver被vins-mono使用，而g2o被orb-slam3使用，因此到底哪个优化器更适合于在slam算法开发中使用？
测试
分别基于这两种优化器实现BA过程，然后针对同一个仿真数据集进行测试，测试程序链接: https://github.com/chennuo0125-HIT/test/tree/main/ceres_g2o_comparer，按readme进行编译运行可得如下结果:
************** solve ba by g2o ***************
# Using EigenSparseCholesky poseDim 6 landMarkDim 3 blockordering 1
Performing full BA:
iteration= 0 chi2= 238086189.574891 time= 0.00782969 cumTime= 0.00782969 edges= 9135 schur= 1 lambda= 5991.771138 levenbergIter= 1
iteration= 1 chi2= 12982651.993440 time= 0.00342487 cumTime= 0.0112546 edges= 9135 schur= 1 lambda= 1997.257046 levenbergIter= 1
iteration= 2 chi2= 657425.312444 time= 0.00318212 cumTime= 0.0144367 edges= 9135 schur= 1 lambda= 665.752349 levenbergIter= 1
iteration= 3 chi2= 124163.255742 time= 0.00287256 cumTime= 0.0173092 edges= 9135 schur= 1 lambda= 221.917450 levenbergIter= 1
iteration= 4 chi2= 75275.286988 time= 0.00304911 cumTime= 0.0203584 edges= 9135 schur= 1 lambda= 73.972483 levenbergIter= 1
iteration= 5 chi2= 34439.999175 time= 0.00264852 cumTime= 0.0230069 edges= 9135 schur= 1 lambda= 24.657494 levenbergIter= 1
iteration= 6 chi2= 22057.399494 time= 0.00309912 cumTime= 0.026106 edges= 9135 schur= 1 lambda= 16.438330 levenbergIter= 1
iteration= 7 chi2= 18344.890824 time= 0.00295518 cumTime= 0.0290612 edges= 9135 schur= 1 lambda= 5.479443 levenbergIter= 1
iteration= 8 chi2= 17868.574026 time= 0.00397518 cumTime= 0.0330364 edges= 9135 schur= 1 lambda= 7.305924 levenbergIter= 2
iteration= 9 chi2= 17333.014982 time= 0.00399876 cumTime= 0.0370351 edges= 9135 schur= 1 lambda= 9.741232 levenbergIter= 2
point error before optimize: 2.96158
point error after optimize: 0.462318
************** solve ba by ceres **************
Solver Summary (v 2.0.0-eigen-(3.3.7)-lapack-suitesparse-(5.7.1)-cxsparse-(3.2.0)-eigensparse-no_openmp)
Original Reduced
Parameter blocks 708 706
Parameters 2184 2170
Effective parameters 2169 2157
Residual blocks 9135 9135
Residuals 18270 18270
Minimizer TRUST_REGION
Sparse linear algebra library SUITE_SPARSE
Trust region strategy LEVENBERG_MARQUARDT
Given Used
Linear solver SPARSE_SCHUR SPARSE_SCHUR
Threads 1 1
Linear solver ordering AUTOMATIC 693,13
Schur structure 2,3,6 2,3,6
Cost:
Initial 1.217168e+09
Final 7.973251e+03
Change 1.217160e+09
Minimizer iterations 7
Successful steps 7
Unsuccessful steps 0
Time (in seconds):
Preprocessor 0.005044
Residual only evaluation 0.004244 (7)
Jacobian & residual evaluation 0.010863 (7)
Linear solver 0.034270 (7)
Minimizer 0.053454
Postprocessor 0.000078
Total 0.058576
Termination: CONVERGENCE (Function tolerance reached. |cost_change|/cost: 4.362464e-08 <= 1.000000e-06)
point error before optimize: 2.96158
point error after optimize: 0.0354349
************** cost time **************
g2o cost time: 68.4571 [ms]
ceres cost time: 63.1568 [ms]
结论
基于"说明"中的仿真数据集，可以初步确定ceres-solver在优化精度和耗时方面都优于g2o.
说明
- 测试程序中的仿真数据来源于 g2o 中的 ba_demo.cpp，g2o测试用例也是基于该程序改的。
- 该测试用例只使用了一组仿真数据集，不足以获得较全面的结论，如果大家有好的测试用例或者别的结论，可以保持交流，我的邮箱: chennuo0125@163.com
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