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You can learn slam step by step，there are lot of tutorials
| Date | Stars |
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| 2026-07-24 | 414 |
| 2026-07-25 | 414 |
| 2026-07-28 | 414 |
| 2026-07-30 | 414 |
| 2026-08-06 | 414 |
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# X-SLAM # 1 Introduction I hope you can learn slam step by step, you will can learn online [X-SLAM documents tutorial](https://ltslam-doc.readthedocs.io/en/latest/index.html). What you choose today determines your tomorrow's life path. Diligence will not make up for your indecision. You can learn slam knowledge by [哔哩哔哩 bilibili ](https://space.bilibili.com/478832908). This video website has a detailed explanation of the code and the corresponding SLAM technology principle. Let's start a happy journey. enjoy.... X-SLAM is an open source C++ demo for learn vision slam and lidar slam. Through open source engineering, we can learn the following knowledge content: * <font color='red'> **C++** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/c%2B%2B/c%2B%2B.html) * <font color='red'> **CMake** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/cmake/cmake.html) * <font color='red'> **Eigen** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/eigen/eigen.html) * <font color='red'> **Math** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/math/math.html) * <font color='red'> **OpenCV** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/opencv/opencv_tutorial.html) * <font color='red'> **Quaternions** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/math/math.html) * <font color='red'> **G2O** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/g2o/g2o_tutorial.html) * <font color='red'> **Pangolin** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/pangolin/pangolin.html) * <font color='red'> **libQGLViewer** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/libQGLViewer/libQGLViewer.html) * <font color='red'> **Ceres Solver** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/tutorial/ceres/ceres_solver_tutorial.html) * <font color='red'> **Kalman Filter** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/kalman_filter/kalman_filter.html) * <font color='red'> **VSLAM** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/vslam/vision_slam.html) * <font color='red'> **VINS Mono( VINS Project)** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/vins/vins.html) * <font color='red'> **MSCKF-VIO( MSCKF Project)** </font> [tutorial](https://ltslam-doc.readthedocs.io/en/latest/msckf/msckf.html) **开源社区文档** * <font color='red'> **X-SLAM Tutorial Documents** </font> [网站链接](https://ltslam-doc.readthedocs.io/en/latest/index.html)  **哔哩哔哩 bilibili 视频教程** * <font color='red'> **B站视频教程** </font> [网站链接](https://space.bilibili.com/478832908)  ## 1.1 <font color='green'>Download LTSLAM source:</font> ```bash git clone https://github.com/quanduyong/LTSLAM.git ``` ## 1.2 <font color='green'>Project directory:</font> ### xlsam_ros (2D lidar SLAM) ```bash # step 1 下载 rosbag 数据集 # https://github.com/cartographer-project/cartographer_ros/blob/master/docs/source/data.rst wget https://storage.googleapis.com/cartographer-public-data/bags/backpack_2d/b2-2015-05-26-13-15-25.bag # step 2 run roslaunch xslam_ros demo_slam2d.launch # step 3 rosbag play rosbag play b2-2015-05-26-13-15-25.bag ```  # 2 安装x-slam ## 2.1 推荐：docker方式安装 ### 2.1.1 docker安装 ```bash cd docker ./scripts/install_docker.sh ``` ### 2.1.2 X-SLAM环境部署和安装 ```bash cd docker ./build_dev.sh standalone.x86_64.dockerfile ``` ### 2.1.3 运行X-SLAM的demos案例 X-SLAM的demo有很多，一下简单运行几个demo ```bash docker run -it xslam/ltslam ``` 运行每个模块的demo * run ceres ``` ./xslam.ceres.helloworld_numeric_diff_test ``` * run g2o ```bash ./xslam.g2o.curve_fitting_test ``` * run dbow3 ``` ./xslam.dbow3.loop_closure_detect_test ``` * run opencv ``` ./xslam.opencv.feature_detection.orb_feature_detector_test ``` * run
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9e9c3faf111c660f, topic:slam, desc:slam, readme:slam
matched fp:9e9c3faf111c660f, topic:tutorial, readme:tutorial