SLAM学习笔记（2）SLAM算法

合集 - SLAM(55)

1.个人资源索引2018-03-032.SLAM学习笔记（1）基本概念2015-08-06

3.SLAM学习笔记（2）SLAM算法2015-08-21

4.[ROS]2 尝试编译OrbSLAM2016-01-185.[ROS]1 小乌龟2016-01-186.SLAM最近的工作2015-11-057.SLAM学习笔记（3）相关概念2015-09-168.[SLAM]2D激光线特征提取2016-07-169.[SLAM] GMapping SLAM源码阅读（草稿）2016-07-0110.[QGLViewer]First Demo2016-06-2911.SLAM数据集2016-06-2312.[SLAM]2D激光扫描匹配方法2016-06-1513.[g2o]一个备忘2016-04-2114.[ROS] slam_gmapping2016-01-2415.[ROS]3 Linux编程练习2016-01-2016.Linux学习和ROS安装（1）2017-12-1917.小豆包的学习之旅：机器人定位2017-05-0218.小豆包的学习之旅：传感器观测模型2017-04-2119.Kinect2.0相机标定2017-04-1020.小豆包的学习之旅：里程计运动模型2017-04-0321.小豆包的学习之旅：入门篇2017-04-0222.Kinect2.0点云数据获取2017-03-3023.[硬件]Robot运动控制2017-03-2224.[硬件]Urg_viewer数据读取2017-03-1425.[硬件]三维点云数据获取2017-03-0426.Kinect2.0获取数据2017-02-1527.ndt histogram_direction2016-09-0828.rplidar & hector slam without odometry2016-08-2629.rplidar测试2016-08-1230.[原创]NDT方法在SLAM中的应用2016-08-0831.[SLAM]Karto SLAM算法学习（草稿）2016-07-2232.ROS学习备忘2018-07-1833.[ROS]激光驱动安装2018-07-1634.OrbSLAM2采集点云数据2018-03-2635.Cartographer源码阅读(8)：imu_tracker2018-03-2136.[硬件]点云数据采集22018-03-1937.Cartographer源码阅读(9)：图优化的前端——闭环检测2018-03-0838.Cartographer源码阅读(7)：轨迹推算和位姿推算的原理2018-03-0839.Ethzasl MSF源码阅读(3)：MSF_Core和PoseMeasurement2018-03-0740.Ethzasl MSF源码阅读(2)：百川汇海2018-03-0641.Ethzasl MSF源码阅读(1)：程序入口和主题订阅2018-03-0442.Cartographer源码阅读(6)：LocalTrajectoryBuilder和PoseExtrapolator2018-02-2543.Cartographer源码阅读(5)：PoseGraph位姿图2018-02-2444.Cartographer源码阅读(4)：Node和MapBuilder对象22018-01-1045.Cartographer源码阅读(3)：程序逻辑结构2017-12-2946.Cartographer源码阅读(2)：Node和MapBuilder对象2017-12-2847.Cartographer源码阅读(1)：程序入口2017-12-2848.实时Cartographer测试(1) - rplidar2017-12-2149.Cartographer安装2017-12-2050.ROS安装（2）2017-12-2051.[ROS]一些传感器数据读取融合问题的思考2017-05-0652.小豆包的学习之旅：占用概率栅格地图和cost-map2017-05-0253.小豆包的学习之旅：开发记录2017-04-2954.[概述]移动机器人自主探索2017-04-1855.MRPT编译2017-04-14

收起

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.

 

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