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SLAM学习笔记（2）SLAM算法
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SLAM算法分为三类：Kalman滤波、概率滤波、图优化
Kalman滤波方法包括EKF、EIF；概率滤波包括RBPF，FastSLAM是RBPF滤波器最为成功的实例, 也是应用最为广泛的SLAM方法；
SLAM分为Full SLAM和Online SLAM
常见的二维激光SLAM算法
1、GMapping is a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data.
2、Tinyslam又称CoreSLAM
The advantage of DP-SLAM over CoreSLAM is thus the thoretical ability not to be lost in long corridors, and this
is the goal indeed of the map-per-particle concept - not the loop closing which can’t be achieved in DP-SLAM without an external process. As a matter of
fact, we decided that this advantage didn’t worth the complexity - especially as we could rely on a good odometry on our platform and given that our goal was to close rather small loops (exploring laboratories instead of corridors...).
As the idea of CoreSLAM was to integrate laser information in our localization subsystem based on particle filter.
The advantage of DP-SLAM over CoreSLAM is thus the thoretical ability not to be lost in long corridors, and this
is the goal indeed of the map-per-particle concept - not the loop closing which can’t be achieved in DP-SLAM without an external process. As a matter of
fact, we decided that this advantage didn’t worth the complexity - especially as we could rely on a good odometry on our platform and given that our goal was to close rather small loops (exploring laboratories instead of corridors...).
As the idea of CoreSLAM was to integrate laser information in our localization subsystem based on particle filter.
3、DPSLAM works by maintaining a joint distribution over robot poses and maps via a particle filter. The algorithm associates a map to each particle, and focuses on the problem of sharing parts of maps among particles in order to minimize memory (and time through map copy). The problem with DP-SLAM is that it is rather complex to integrate into an existing particle filter based localization susbystem
4、Hector-SLAM
5、Karto-SLAM
http://www.zhihu.com/question/35116055/answer/62001013
http://blog.csdn.net/dourenyin/article/details/48055441
视觉SLAM算法
1、orbslam 是14-15年被一个西班牙博士做的，目前还在做，最近又发了新文章：Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM。
orbslam可以看做是PTAM的一个延伸。ptam想必做visual slam 的都知道，它是第一个将tracking和mapping分成两个线程实现的实时slam系统，07年出来的时候很惊艳。几乎成立后来feature-based slam方法的标准。orbslam 算是这个思路的延伸，于ptam相比它又加了一个loopclosing的线程。这个系统基于ptam，个人感觉效果也更好一些（毕竟ptam相对较老），整合了covisible graph，基于bagofwords 做relocalization等技术。
常见的一些开源代码（高博整理）：[转载]
* rtabslam https://github.com/introlab/rtabmap_ros#rtabmap_ros
* ORB-slam https://github.com/raulmur/ORB_SLAM
* rgbd-slam-v2 https://github.com/felixendres/rgbdslam_v2
* lsd-slam https://github.com/tum-vision/lsd_slam
* dvo-slam https://github.com/tum-vision/dvo_slam
* hector-slam https://github.com/tu-darmstadt-ros-pkg/hector_slam
* svo https://github.com/uzh-rpg/rpg_svo
SLAM研究体系分类
