base on Code exercises for the SLAM courses # fastcampus_slam_codes This repository contains code exercises for the following lecture series provided by @changh95 at FastCampus: - ['Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving'](https://github.com/changh95/fastcampus_slam_codes/tree/main#zero-to-hero-slam-lectures-for-physical-ai-and-3d-computer-vision) - ['SLAM Zero-to-Hero series for Physical AI and 3D Computer Vision'](https://github.com/changh95/fastcampus_slam_codes/tree/main#computer-vision-lidar-processing-and-sensor-fusion-for-autonomous-driving) > Actively reworking the repository now. Stay tuned, because A LOT OF NEW TUTORIALS are on the way! </b> ## Zero-to-Hero SLAM lectures for Physical AI and 3D Computer Vision  The course can be found [here](https://fastcampus.co.kr/data_online_slam). The course content is essentially a superset of 'Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving', but with a more general focus within robotics, drones, AR/VR, autonomous driving. ### Table of Contents - Chapter 1: Introduction to SLAM - 1.1 Lecture introduction - 1.2 Mobile robotics - 1.3 What is SLAM? - 1.4 Hardware used in SLAM - 1.5 Types of SLAM - 1.6 Applications of SLAM - 1.7 Tips for studying SLAM - 1.8 C++ and SLAM - 1.9 [Basic C++ programming](SLAM_zero_to_hero/1_9) - 1.10 [Building C++ libraries](SLAM_zero_to_hero/1_10) - 1.11 [C++ CPU profiler](SLAM_zero_to_hero/1_11) - 1.12 [C++ memory profiler](SLAM_zero_to_hero/1_12) - 1.13 Python basics - 1.14 [Basic Python programming](SLAM_zero_to_hero/1_14) - 1.15 [PyBind](SLAM_zero_to_hero/1_15) - 1.16 [ROS fundamentals](SLAM_zero_to_hero/1_16) - 1.17 Rotation and translation in 3D space - 1.18 Homogeneous coordinates - 1.19 Lie Group - 1.20 Basics of Lie algebra - 1.21 [Eigen + Sophus library hands-on](SLAM_zero_to_hero/1_21) - 1.22 Continuous-time representation - 1.23 Camera basics for robotics - 1.24 Camera models - 1.25 LiDAR basics - 1.26 IMU basics - 1.27 Radar basics - 1.28 Forward/Inverse kinematics - 1.29 Sensor calibration - 1.30 [Kalibr package hands-on](SLAM_zero_to_hero/1_30) - Chapter 2: Dive into SLAM (Front-end) - 2.1 Part 2 introduction - 2.2 Local feature detection - 2.3 [Classical local feature detection hands-on](SLAM_zero_to_hero/2_3) - 2.4 [Deep local feature detection hands-on](SLAM_zero_to_hero/2_4) - 2.5 Feature tracking basics - 2.6 Advanced feature tracking in practice - 2.7 [Feature tracking hands-on](SLAM_zero_to_hero/2_7) - 2.8 Global feature detection - 2.9 [Global feature detection hands-on](SLAM_zero_to_hero/2_9) - 2.10 [Deep global feature detection hands-on](SLAM_zero_to_hero/2_10) - 2.11 Epipolar geometry - 2.12 [Epipolar geometry hands-on](SLAM_zero_to_hero/2_12) - 2.13 Homography - 2.14 [Homography hands-on](SLAM_zero_to_hero/2_14) - 2.15 [MonoVO hands-on](SLAM_zero_to_hero/2_15) - 2.16 Triangulation - 2.17 [Triangulation hands-on](SLAM_zero_to_hero/2_17) - 2.18 Perspective-n-points - 2.19 [Perspective-n-points hands-on](SLAM_zero_to_hero/2_19) - 2.20 RANSAC - 2.21 Advanced RANSAC - 2.22 [RANSAC hands-on](SLAM_zero_to_hero/2_22) - 2.23 M-estimator & MAXCON - 2.24 What is point cloud? - 2.25 Introduction to PCL library - 2.26 Point cloud preprocessing - 2.27 [Point cloud preprocessing hands-on](SLAM_zero_to_hero/2_27) - 2.28 ICP - 2.29 [ICP hands-on](SLAM_zero_to_hero/2_29) - 2.30 [Advanced ICP hands-on](SLAM_zero_to_hero/2_30) - 2.31 [Octree, Octomap, Bonxai hands-on](SLAM_zero_to_hero/2_31) - Chapter 3: Dive into SLAM (Back-end) - 3.1 Part 3 introduction - 3.2 Factor graph - 3.3 Nonlinear least squares - 3.4 Nonlinear optimization - 3.5 Optimization on manifolds - 3.6 Graph-based SLAM - 3.7 Schur complement - 3.8 Auto-diff - 3.9 Continuous-time optimization - 3.10 Sparsity in SLAM - 3.11 Bundle adjustment - 3.12 Nonlinear solvers - 3.13 [g2o hands-on](SLAM_zero_to_hero/3_13) - 3.14 [GTSAM hands-on](SLAM_zero_to_hero/3_14) - 3.15 [Ceres-solver hands-on](SLAM_zero_to_hero/3_15) - 3.16 [SymForce hands-on](SLAM_zero_to_hero/3_16) - 3.17 SLAM systems - 3.18 Various map representations - 3.19 VSLAM system architecture - 3.20 LiDAR SLAM system architecture - 3.21 RADAR SLAM system architecture - 3.22 Event SLAM system architecture - 3.23 Inertial odometry basics - 3.24 Leg odometry basics - 3.25 Sensor fusion - Chapter 4: Classical SLAM - 4.1 Part 4 introduction - 4.2 Feature-based VSLAM - 4.3 Direct VSLAM - 4.4 Visual-inertial odometry - 4.5 2D LiDAR SLAM - 4.6 3D LiDAR SLAM - 4.7 Sensor fusion SLAM - 4.8 ORB-SLAM 2 - 4.9 Basalt-VIO - 4.10 Cartographer - 4.11 KISS-SLAM - 4.12 GLIM - 4.13 FAST-LIO2 - 4.14 FAST-LIVO2 - Chapter 5: Advanced SLAM - AI Integration and Hardware Optimization - 5.1 Part 5 introduction - 5.2 SLAM + Object detection + Segmentation - 5.3 SLAM + Depth estimation - 5.4 SLAM + Camera pose regression - 5.5 SLAM + Deep feature matching - 5.6 SLAM + Deep optical flow / scene flow - 5.7 SLAM + Differentiable bundle adjustment - 5.8 SLAM + Feed-forward 3D transformer - 5.9 SLAM + NeRF / Implicit neural field - 5.10 SLAM + Gaussian Splatting - 5.11 SLAM + Video generation - 5.12 SLAM + VLM/VLA - 5.13 SLAM + 3D Scene graph - 5.14 SLAM + Certifiably optimal algorithm - 5.15 SLAM + Auto-encoder / diffusion - 5.16 SLAM + Graph processor - 5.17 DSP-SLAM - 5.18 Kimera - 5.19 ConceptFusion - 5.20 Gaussian Splatting SLAM - 5.21 MASt3r-SLAM - 5.22 PIN-SLAM - 5.23 Suma++ - 5.24 Differences between desktop, server, and embedded boards - 5.25 Characteristics of real-time SLAM - 5.26 Characteristics of auto-labeling / data-crunching SLAM - 5.27 C++ build configuration optimization - 5.28 SIMD acceleration and CPU optimization techniques - 5.29 [SIMD acceleration hands-on](SLAM_zero_to_hero/5_29) - 5.30 Introduction to NVIDIA Jetson - 5.31 [CUDA acceleration hands-on](SLAM_zero_to_hero/5_31) - Final projects - Project 1: SLAM for autonomous driving - Project 2: SLAM for drones - Project 3: SLAM for mobile scanner systems - Project 4: SLAM for quadruped robots - Project 5: SLAM for humanoid robots - Project 6: SLAM for VR/AR headsets ### Libraries in Base Docker Image | Library | Description | |---------|-------------| | **OpenCV 4.12** (with contrib) | Computer vision, feature detection (ORB, SIFT, TEBLID), ArUco markers | | **Eigen 5.0** | Linear algebra, matrix operations | | **Sophus** | Lie groups (SO3, SE3) for robotics | | **Ceres Solver** | Nonlinear least squares optimization | | **g2o** | Graph-based optimization for SLAM | | **GTSAM** | Factor graph optimization | | **PoseLib** | Minimal pose solvers (P3P, 5-point, homography) | | **OpenGV** | Geometric vision algorithms (relative/absolute pose, triangulation) | | **PCL** | Point cloud processing | | **Pangolin** | 3D visualization | | **easy_profiler** | CPU profiling with GUI | | **SymForce** | Symbolic computation for robotics | | **Rerun** | Modern 3D visualization for robotics | ## Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving  The course can be found [here](https://fastcampus.co.kr/data_online_autovehicle). This course contains the following 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](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/1_7) - Chapter 2: Introduction 3D Spaces - 2.1 3D rotation and translation - 2.2 [3D rotation and translation, using Eigen library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/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](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/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](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/3_2) - 3.3 [Superpoint and Superglue, using C++ and TensorRT](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/3_3) - 3.4 Global feature extraction - 3.5 [Bag of Visual Words, using DBoW2 library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/3_5) - 3.6 [Learning-based global feature extraction, using PyTorch and Tensorflow libraries](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/3_6) - 3.7 Feature tracking - 3.8 [Optical flow, using OpenCV library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/3_8) - Chapter 4: Point cloud processing - 4.1 Introduction to point cloud processing - 4.2 [Introduction to point cloud processing, using PCL library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/4_2) - 4.3 Point cloud pre-processing - 4.4 [Point cloud pre-processing, using PCL library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/4_4) - 4.5 Iterative closest point - 4.6 [Iterative closest point, using PCL library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/4_6) - 4.7 Advanced ICP methods - 4.8 [Advanced ICP methods (G-ICP, NDT, TEASER++, KISS-ICP), using PCL library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/4_8) - 4.9 [Octree, Octomap, Bonxai, using PCL/Octomap/Bonxai libraries](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/4_9) - Chapter 5: Multiple view geometry - 5.1 Epipolar geometry - 5.2 [Essential and Fundamental matrix estimation, using OpenCV library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/5_2) - 5.3 Homography - 5.4 [Bird's eye view (BEV) projection, using OpenCV library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/5_4) - 5.5 [Simple monocular visual odometry, using OpenCV library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/5_5) - 5.6 Triangulation - 5.7 [Triangulation, using OpenCV library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/5_7) - 5.8 Perspective-n-Points (PnP) and Direct Linear Transform (DLT) - 5.9 [Fiducial marker tracking, using OpenCV library](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/5_9) - 5.10 RANSAC - 5.11 Advanced RANSAC methods (USAC) - 5.12 [RANSAC and USAC, using OpenCV and RansacLib libraries](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/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](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/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](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/orb_slam2), [ORB-SLAM3](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/orb_slam3) - 6.6 [DynaVINS](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/dynavins) - 6.7 [CubeSLAM](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/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](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/hdl_graph_slam) - 7.4 [KISS-ICP](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/kiss_icp) - 7.5 [SHINE-Mapping](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/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](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/dsp_slam) - [Suma++](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/suma_plus_plus) - [Cartographer-KITTI](Computer_Vision_LiDAR_Processing_and_Sensor_Fusion_for_Autonomous_Driving/cartographer) ", 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"