library: OpenGV
pages:   https://laurentkneip.github.io/opengv
brief:   OpenGV is a collection of computer vision methods for solving
         geometric vision problems. It contains absolute-pose, relative-pose,
         triangulation, and point-cloud alignment methods for the calibrated
         case. All problems can be solved with central or non-central cameras,
         and embedded into a random sample consensus or nonlinear optimization
         context. Matlab and Python interfaces are implemented as well. The link
         to the above pages also shows links to precompiled Matlab mex-libraries.
         Please consult the documentation for more information.
author:  Laurent Kneip, The Australian National University
contact: kneip.laurent@gmail.com
https://laurentkneip.github.io/opengv/page_installation.html
git clone https://github.com/laurentkneip/opengv
sudo apt-get install build-essential
sudo apt-get install cmake
sudo apt-get install cmake libeigen3-dev
mkdir build && cd build && cmake .. && make
2-entity RANSAC:
author:  Yanmei Jiao, Zhejiang University
contact: ymjiao@zju.edu.cn
for monocular camera pose estimation: 
1. 2P --> sac_problems::absolute_pose::AbsolutePoseSacProblem::TWOE2P
2. 1P1L --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::TWOE1P1L
3. Mixed --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::TWOEMixed
for multiple camera pose estimaion:
1. MC2P --> sac_problems::absolute_pose::AbsolutePoseSacProblem::MC2P
2. MC1P1L --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::MC1P1L
3. MCMixed --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::MCMixed