SLAM技术传统教学模式记录（转）

首先，说明一下我并不熟悉SLAM理论，也不感兴趣。

阅读了一些资料，传统SLAM学习方法大致如下：

• openSLAM官网研读算法原始论文
• 理解算法基础上阅读开源代码
• 将其应用到具体实践中
• 发现参数或其他问题优化改进，给出更好的方案

这里以gmapping为例吧？

前期工作：

参考：openslam-org.github.io/gmapping.html

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

Authors Giorgio Grisetti; Cyrill Stachniss; Wolfram Burgard;

Get the Source Code! 源码在这里！！！

Long Description Recently Rao-Blackwellized particle filters have been introduced as effective means to solve the simultaneous localization and mapping (SLAM) problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. We present adaptive techniques to reduce the number of particles in a Rao- Blackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decrease the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we apply an approach to selectively carry out re-sampling operations which seriously reduces the problem of particle depletion.

Input Data The approach takes raw laser range data and odometry. This version is optimized for long-range laser scanners like SICK LMS or PLS scanner. Short range lasers like Hokuyo scanner will not work that well with the standard parameter settings.

Logfile Format Carmen log format

Type of Map grid maps

Hardware/Software Requirements Linux/Unix, GCC 3.3/4.0.x CARMEN (latest version) Quick Install-Guide using bash: ./configure; . ./setlibpath; make;

Papers Describing the Approach 论文在这里！！！ Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard: Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters, IEEE Transactions on Robotics, Volume 23, pages 34-46, 2007 (link) Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard: Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling, In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2005 (link)

Further Reading A. Doucet: On sequential simulation-based methods for bayesian filtering, Technical report, Signal Processing Group, Dept. of Engeneering, University of Cambridge, 1998

License Information This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. The authors allow the users of OpenSLAM.org to use and modify the source code for their own research. Any commercial application, redistribution, etc has to be arranged between users and authors individually and is not covered by OpenSLAM.org. GMapping is licenced under BSD-3-Clause

Further Information The SLAM approach is available as a library and can be easily used as a black box. Making changes to the algorithm itself, however, requires quite some C++ experience.

Further Links French translation of this page (external link!). Belorussian translation of this page (external link!). Polish translation of this page (external link!).

*** OpenSLAM.org is not responsible for the content of this webpage *** *** Copyright and V.i.S.d.P.: Giorgio Grisetti; Cyrill Stachniss; Wolfram Burgard; ***

后期工作：

ROS1：wiki.ros.org/gmapping

案例：wiki.ros.org/stdr_simulator/Tutorials/Create%20a%20map%20with%20gmapping

ROS2：github.com/Project-MANAS/slam_gmapping

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按如上步骤将SLAM算法用于机器人上～

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