SLAM算法分爲三類：Kalman濾波、概率濾波、圖優化
博主地址：
https://www.cnblogs.com/yhlx125/p/4814232.html
Kalman濾波方法包括EKF、EIF；概率濾波包括RBPF，FastSLAM是RBPF濾波器最爲成功的實例, 也是應用最爲廣泛的SLAM方法；
SLAM分爲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年出來的時候很驚豔。幾乎成立後來feature-based slam方法的標準。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：
SIFT，即尺度不變特徵變換（Scale-invariant feature transform，SIFT）
SIFT是用於圖像處理領域的一種描述子。這種描述具有尺度不變性，可在圖像中檢測出關鍵點，是一種局部特徵描述子。
[1] 該方法於1999年由David Lowe
[2] 首先發表於計算機視覺國際會議（International Conference on Computer Vision，ICCV），2004年再次經David Lowe整理完善後發表於International journal of computer vision（IJCV）
[3] 。截止2014年8月，該論文單篇被引次數達25000餘次。
點雲
Point Cloud Library (PCL) 是一個獨立的大型的處理二維/三維圖像和點雲數據的開源工程，由Willow Garage公司開發，起初只是以PR2機器人在3D數據感知算法處理上速度更快爲目標，後來才漸漸發展爲一個獨立的函數庫。PCL包含了許多先進算法，比如濾波，特徵估計，表面重建，模型擬合和分割等等。
http://pointclouds.org/
CARMEN - The Carnegie Mellon Robot Navigation Toolkit
CARMEN is an open-source collection of software for mobile robot control.
CARMEN is an open-source collection of software for mobile robot control. CARMEN is modular software designed to provide basic navigation primatives including: module communication infrastructure, base and sensor control, obstacle avoidance, localization, path planning, and mapping.
http://carmen.sourceforge.net/
隨機採樣一致性 （ Random Sample Consensus， RANSAC）
ICP算法（Iterative Closest Point迭代最近點）
ICP（Iterative Closest Point迭代最近點）算法是一種點集對點集配準方法
而ICP是最廣泛應用的配準方法，也就是KinectFusion論文中所提到的ICP（ Iterative Closest Point ）， 最近鄰迭代算法。icp利用迭代一步步地算出正確對應關係。
Structure from Motion (SfM) 運動恢復結構
