base on Code exercises for the SLAM course in 'Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving' lecture series # fastcampus_slam_codes This repository contains code exercises for the SLAM section in the lecture series - ['Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving'](https://fastcampus.co.kr/data_online_autovehicle) at FastCampus. This lecture series is delivered in Korean language.  ## How to use Most of the code exercises are based on the base docker image. The base docker image contains numerous C++ libraries for SLAM, such as OpenCV, Eigen, Sophus, PCL, and ceres-solver. You can build the base docker image using the following command. ```shell docker build . --tag slam:latest --progress=plain echo "xhost +local:docker" >> ~/.profile ``` ## Table of contents - Chapter 1: Introduction to SLAM - 1.1 Lecture introduction - 1.2 What is SLAM? - 1.3 Hardware for SLAM - 1.4 Types of SLAM - 1.5 Applications of SLAM - 1.6 Before we begin... - 1.7 [Basic C++ / CMake](1_7) - Chapter 2: Introduction 3D Spaces - 2.1 3D rotation and translation - 2.2 [3D rotation and translation, using Eigen library](2_2) - 2.3 Homogeneous coordinates - 2.4 Lie Group - 2.5 Basic Lie algebra - 2.6 [Lie Group and Lie algebra, using Sophus library](2_6) - 2.7 How cameras work - 2.8 How LiDARs work - Chapter 3: Image processing - 3.1 Local feature extraction & matching - 3.2 [Local feature extraction & matching, using OpenCV library](3_2) - 3.3 [Superpoint and Superglue, using C++ and TensorRT](3_3) - 3.4 Global feature extraction - 3.5 [Bag of Visual Words, using DBoW2 library](3_5) - 3.6 [Learning-based global feature extraction, using PyTorch and Tensorflow libraries](3_6) - 3.7 Feature tracking - 3.8 [Optical flow, using OpenCV library](3_8) - Chapter 4: Point cloud processing - 4.1 Introduction to point cloud processing - 4.2 [Introduction to point cloud processing, using PCL library](4_2) - 4.3 Point cloud pre-processing - 4.4 [Point cloud pre-processing, using PCL library](4_4) - 4.5 Iterative closest point - 4.6 [Iterative closest point, using PCL library](4_6) - 4.7 Advanced ICP methods - 4.8 [Advanced ICP methods (G-ICP, NDT, TEASER++, KISS-ICP), using PCL library](4_8) - 4.9 [Octree, Octomap, Bonxai, using PCL/Octomap/Bonxai libraries](4_9) - Chapter 5: Multiple view geometry - 5.1 Epipolar geometry - 5.2 [Essential and Fundamental matrix estimation, using OpenCV library](5_2) - 5.3 Homography - 5.4 [Bird's eye view (BEV) projection, using OpenCV library](5_4) - 5.5 [Simple monocular visual odometry, using OpenCV library](5_5) - 5.6 Triangulation - 5.7 [Triangulation, using OpenCV library](5_7) - 5.8 Perspective-n-Points (PnP) and Direct Linear Transform (DLT) - 5.9 [Fiducial marker tracking, using OpenCV library](5_9) - 5.10 RANSAC - 5.11 Advanced RANSAC methods (USAC) - 5.12 [RANSAC and USAC, using OpenCV and RansacLib libraries](5_12) - 5.13 Graph-based SLAM - 5.14 Least squares - 5.15 Schur complement - 5.16 Bundle adjustment - 5.17 [Bundle adjustment, using Ceres-Solver library](5_17) - Chapter 6: Visual-SLAM - 6.1 Overview of feature-based VSLAM - 6.2 Overview of direct VSLAM - 6.3 Overview of visual-inertial odometry (VIO) - 6.4 Spatial AI - 6.5 [ORB-SLAM2](orb_slam2), [ORB-SLAM3](orb_slam3) - 6.6 [DynaVINS](dynavins) - 6.7 [CubeSLAM](cubeslam) - Chapter 7: LiDAR SLAM - 7.1 Overview of 2D LiDAR SLAM - 7.2 Overview of 3D LiDAR SLAM and LiDAR-inertial odometry - 7.3 [HDL-Graph-SLAM](hdl_graph_slam) - 7.4 [KISS-ICP](kiss_icp) - 7.5 [SHINE-Mapping](shine_mapping) - Chapter 8: CI/CD for SLAM - 8.1 TDD and tests - 8.2 CI/CD - 8.3 CI agents - 8.4 CI/CD for Python SLAM projects - 8.5 CI/CD for C++ SLAM projects - Final projects: - [DSP-SLAM](dsp_slam) - [Suma++](suma_plus_plus) - [Cartographer-KITTI](cartographer) ## Acknowledgements ORB-SLAM 2/3 authors, DynaVINS authors, CubeSLAM authors, HDL-Graph-SLAM authors, KISS-ICP authors, SHINE-Mapping authors, and all the authors of the libraries used in this repository. ## Contributors Thanks goes to these wonderful people ❤️: - [Juwon Jason Kim](https://github.com/U-AMC), for building the 'Cartographer-KITTI' examples - [Hyungtae Lim](https://github.com/LimHyungTae), for his PCL tutorial code snippets <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tr> <td align="center"><a href="https://github.com/U-AMC"><img src="https://avatars.githubusercontent.com/u/43529281?v=4" width="100px;" alt=""/><br />_{<b>Juwon Jason Kim </b>}</a><br /><a href="https://github.com/U-AMC" title="GitHub"> :octocat:</a></td> <td align="center"><a href="https://github.com/LimHyungTae"><img src="https://avatars.githubusercontent.com/u/35317311?v=4" width="100px;" alt=""/><br />_{<b>Hyungtae Lim </b>}</a><br /><a href="https://github.com/LimHyungTae" title="GitHub">:octocat:</a></td> </tr> </table> <!-- markdownlint-restore --> <!-- prettier-ignore-end --> <!-- ALL-CONTRIBUTORS-LIST:END --> ", Assign "at most 3 tags" to the expected json: {"id":"5779","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"