基于优化方法的机器人同步定位与地图创建(SLAM)后端(Back-end)设计技术收集

基于优化方法的机器人同步定位与地图创建(SLAM)
后端(Back-end)设计技术收集
Sason@CSDN
持续更新中。
当前更新日期：2013.04.27

学习SLAM首推2个网站：
1. WIKI上的SLAM介绍与资源总结：http://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping
2. http://www.openslam.org/
1. The pose graph of Olson
http://rvsn.csail.mit.edu/graphoptim/
2. TreeMap
http://www.openslam.org/treemap.html

Treemap is an algorithm for feature based Gaussian SLAM. Actually it is an algorithm for incremental probabilistic inference in a high dimensional Gaussian defined as the product of many low dimensional Gaussians (incremental
least square). Treemap can handle different variants of SLAM. Everything, that's specific to a SLAM variant or even to SLAM as a problem is contained in a small driver layer that can be adapted by the user.

3. TORO
http://www.openslam.org/toro.html
TORO is an optimization approach for constraint-network. It provides an efficient, gradient descent-based error minimization procedure. There is a 2D and a 3D version of TORO available.

4. Square Root SAM
http://www.cc.gatech.edu/~kaess/pub/Dellaert06ijrr.html
5. iSAM and iSAM2
http://people.csail.mit.edu/kaess/isam/
http://people.csail.mit.edu/kaess/pub/Kaess12ijrr.html

iSAM is a general optimization library for incremental sparse nonlinear problems as encountered in simultaneous localization and mapping (SLAM).

6. Sparse Pose Ajustment
http://www.ros.org/research/2010/spa/

7. g2o
http://www.openslam.org/g2o.html
g2o is an open-source C++ framework for optimizing graph-based nonlinear error functions. g2o has been designed to be easily extensible to a wide range of problems and a new problem typically can be specified in
a few lines of code. The current implementation provides solutions to several variants of SLAM and BA.

8.
Vertigo
http://www.tu-chemnitz.de/etit/proaut/forschung/robustSLAM.html.en
Vertigo is a C++ extension for g2o and gtam. It provides an implementation of switchable constraints and enables g2o and gtsam to solve pose graph SLAM problems despite the presence of false positive loop closure constraints.

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