【GTSAM】GTSAM/iSAM1/2资源整理

1 what GTSAM ?

GTSAM 是一个在机器人领域和计算机视觉领域用于平滑（smoothing）和建图（mapping）的C++库。它与g2o不同的是，g2o采用稀疏矩阵的方式求解一个非线性优化问题，而GTSAM是采用因子图（factor graphs）和贝叶斯网络（Bayes networks）的方式最大化后验概率。

1、书本和视频资源

“Incremental Smoothing and Mapping”（下载链接：http://www.cs.cmu.edu/~kaess/pub/Kaess08tro.pdf）.
M. Kaess的公开课视频（含有中英文字幕）。【B站：https://www.bilibili.com/video/BV1gk4y1R7Lb】【油管：https://www.youtube.com/watch?v=_W3Ua1Yg2fk】
深蓝学院有一个董靖博士的公开课，个人觉得讲的很好。【深蓝学院：https://www.shenlanxueyuan.com/open/course/28】
B站上有南大计算机黄宇教授关于图优化的视频。【B站：https://www.bilibili.com/video/BV1Y54y197VW】

2、开源代码

miniSAM 【github:https://github.com/dongjing3309/minisam】
iSAM【github:https://github.com/ori-drs/isam】
GTSAM【github:https://github.com/borglab/gtsam】
github: https://github.com/borglab/gtsam
docment: https://gtsam.org/doxygen/a04096.html

【泡泡机器人公开课】第五十六课：gtsam解析 by 董靖 https://www.sohu.com/a/133646392_715754
G2o，GTSAM，Ceres，Tensorflow优化器的方法比较 https://blog.csdn.net/ziliwangmoe/article/details/86561157
GTSAM的安装与使用 https://blog.csdn.net/missiledefense/article/details/56319421
GTSAM的学习笔记 https://blog.csdn.net/qq_27262241/article/details/84291471

3、论文资源（前三篇主要是关于因子图优化的介绍，后两篇主要是关于因子图的应用。）

[1] S. Thrun and J. J. Leonard, “Square Root SAM: Simultaneous Simultaneous Localization and Mapping,” Robotics, pp. 871–889, 2008, doi: 10.1007/978-3-540-30301-5_38.
【论文：https://www.cc.gatech.edu/~dellaert/pub/Dellaert06ijrr.pdf】

[2] M. Kaess, A. Ranganathan, and F. Dellaert, “iSAM: Incremental smoothing and mapping,” IEEE Trans. Robot., vol. 24, no. 6, pp. 1365–1378, 2008, doi: 10.1109/TRO.2008.2006706
【论文https://smartech.gatech.edu/bitstream/handle/1853/26572/kaess_michael_200812_phd.pdf?sequence=1&isAllowed=y】.

[3] C. Robotics et al., “iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering The MIT Faculty has made this article openly available . Please share how this access benefits you . Your story matters . Citation Kaess , Michael et ,” 2016.
【论文：https://www.cs.cmu.edu/~kaess/pub/Kaess12ijrr.pdf】

[4] R. F. Salas-moreno, R. A. Newcombe, P. H. J. Kelly, and A. J. Davison, “SLAM ++: Simultaneous Localisation and Mapping at the Level of Objects,” 2014.
【论文：https://ieeexplore.ieee.org/document/6619022】

[5] X. Wang, R. Marcotte, G. Ferrer, and E. Olson, “AprilSAM: Real-time Smoothing and Mapping,” Icra, pp. 2486–2493, 2018.
【论文：https://ieeexplore.ieee.org/document/8461072】

https://journals.sagepub.com/doi/abs/10.1177/0278364906072768

https://link.zhihu.com/?target=https%3A//www.aaai.org/Papers/IJCAI/2007/IJCAI07-343.pdf

https://ieeexplore.ieee.org/abstract/document/4682731

https://link.zhihu.com/?target=https%3A//www.sciencedirect.com/science/article/pii/S0921889009000864

https://link.zhihu.com/?target=https%3A//journals.sagepub.com/doi/abs/10.1177/0278364911430419

https://link.zhihu.com/?target=https%3A//www.sciencedirect.com/science/article/pii/S092188901300081X

4 ref:

https://blog.csdn.net/QLeelq/article/details/111368277
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