SLAM學習筆記（2）SLAM算法

SLAM算法分為三類：Kalman濾波、概率濾波、圖優化

Kalman濾波方法包括EKF、EIF；概率濾波包括RBPF，FastSLAM是RBPF濾波器最為成功的實例, 也是應用最為廣泛的SLAM方法；

SLAM分為Full SLAM和Online SLAM

常見的二維激光SLAM算法

1、GMapping is a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data.

2、Tinyslam又稱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.

 

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

視覺SLAM算法

1、orbslam 是14-15年被一個西班牙博士做的，目前還在做，最近又發了新文章：Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM。
orbslam可以看做是PTAM的一個延伸。ptam想必做visual slam 的都知道，它是第一個將tracking和mapping分成兩個線程實現的實時slam系統，07年出來的時候很驚艷。幾乎成立后來feature-based slam方法的標准。orbslam 算是這個思路的延伸，於ptam相比它又加了一個loopclosing的線程。這個系統基於ptam，個人感覺效果也更好一些（畢竟ptam相對較老），整合了covisible graph，基於bagofwords 做relocalization等技術。

常見的一些開源代碼（高博整理）：[轉載]
* 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研究體系分類