SLAM技术传统教学模式记录（转）

最新推荐文章于 2025-03-27 10:47:21 发布

原创最新推荐文章于 2025-03-27 10:47:21 发布 · 1.8k 阅读

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本文详细介绍了GMapping——一种高效Rao-Blackwellized粒子滤波算法，用于从激光雷达数据中学习栅格地图。该算法由Giorgio Grisetti等人提出，旨在解决SLAM问题，通过减少粒子数量并采用自适应技术提高地图学习效率。文章还提供了源代码、论文链接及在ROS中的使用教程。

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; *