[1] Comparison of Optimization Techniques for 3D Graph-based SLAM
综述性论文，针对Graph SLAM的总体框架、前端、后端分别进行描述。
- 前端分类为map to map (构建submap), image to map（通过点特征确定相机相对于map的位姿）, image to image (相机图像匹配，simplest)；
- 后端中主要使用了三种工具：G2O（General (Hyper) Graph Optimization）、GTSAM（Georgia Tech Smoothing and Mapping）和HOG-Man（Hierarchical Optimization on Manifolds）
G2O: It is a C++ framework for performing the optimization of nonlinear least squares problems that can be embedded as a graph or in an hyper-graph. (工程常用)
GTSAM: It is a C++ library based on factor graphs. A factor graph consists of factors connected to variables. The factors represent probabilistic information on the unknown random variables in the estimation problem. （代码具有优秀的编程规范，适合学习， iSAM 求解器）
HOG-Man: It applies Gauss Newton with sparse Cholesky factorization that considers a manifold representation of the state space to better deal with the camera rotations. （划分submap，多层图结构）