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'
• 'SLAM Zero-to-Hero series for Physical AI and 3D Computer Vision'

Actively reworking the repository now. Stay tuned, because A LOT OF NEW TUTORIALS are on the way!

Zero-to-Hero SLAM lectures for Physical AI and 3D Computer Vision

The course can be found here.

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
  • 1.10 Building C++ libraries
  • 1.11 C++ CPU profiler
  • 1.12 C++ memory profiler
  • 1.13 Python basics
  • 1.14 Basic Python programming
  • 1.15 PyBind
  • 1.16 ROS fundamentals
  • 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
  • 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
• 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
  • 2.4 Deep local feature detection hands-on
  • 2.5 Feature tracking basics
  • 2.6 Advanced feature tracking in practice
  • 2.7 Feature tracking hands-on
  • 2.8 Global feature detection
  • 2.9 Global feature detection hands-on
  • 2.10 Deep global feature detection hands-on
  • 2.11 Epipolar geometry
  • 2.12 Epipolar geometry hands-on
  • 2.13 Homography
  • 2.14 Homography hands-on
  • 2.15 MonoVO hands-on
  • 2.16 Triangulation
  • 2.17 Triangulation hands-on
  • 2.18 Perspective-n-points
  • 2.19 Perspective-n-points hands-on
  • 2.20 RANSAC
  • 2.21 Advanced RANSAC
  • 2.22 RANSAC hands-on
  • 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
  • 2.28 ICP
  • 2.29 ICP hands-on
  • 2.30 Advanced ICP hands-on
  • 2.31 Octree, Octomap, Bonxai hands-on
• 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
  • 3.14 GTSAM hands-on
  • 3.15 Ceres-solver hands-on
  • 3.16 SymForce hands-on
  • 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
  • 5.30 Introduction to NVIDIA Jetson
  • 5.31 CUDA acceleration hands-on
• 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.

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
• Chapter 2: Introduction 3D Spaces
  • 2.1 3D rotation and translation
  • 2.2 3D rotation and translation, using Eigen library
  • 2.3 Homogeneous coordinates
  • 2.4 Lie Group
  • 2.5 Basic Lie algebra
  • 2.6 Lie Group and Lie algebra, using Sophus library
  • 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
  • 3.3 Superpoint and Superglue, using C++ and TensorRT
  • 3.4 Global feature extraction
  • 3.5 Bag of Visual Words, using DBoW2 library
  • 3.6 Learning-based global feature extraction, using PyTorch and Tensorflow libraries
  • 3.7 Feature tracking
  • 3.8 Optical flow, using OpenCV library
• Chapter 4: Point cloud processing
  • 4.1 Introduction to point cloud processing
  • 4.2 Introduction to point cloud processing, using PCL library
  • 4.3 Point cloud pre-processing
  • 4.4 Point cloud pre-processing, using PCL library
  • 4.5 Iterative closest point
  • 4.6 Iterative closest point, using PCL library
  • 4.7 Advanced ICP methods
  • 4.8 Advanced ICP methods (G-ICP, NDT, TEASER++, KISS-ICP), using PCL library
  • 4.9 Octree, Octomap, Bonxai, using PCL/Octomap/Bonxai libraries
• Chapter 5: Multiple view geometry
  • 5.1 Epipolar geometry
  • 5.2 Essential and Fundamental matrix estimation, using OpenCV library
  • 5.3 Homography
  • 5.4 Bird's eye view (BEV) projection, using OpenCV library
  • 5.5 Simple monocular visual odometry, using OpenCV library
  • 5.6 Triangulation
  • 5.7 Triangulation, using OpenCV library
  • 5.8 Perspective-n-Points (PnP) and Direct Linear Transform (DLT)
  • 5.9 Fiducial marker tracking, using OpenCV library
  • 5.10 RANSAC
  • 5.11 Advanced RANSAC methods (USAC)
  • 5.12 RANSAC and USAC, using OpenCV and RansacLib libraries
  • 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
• 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-SLAM3
  • 6.6 DynaVINS
  • 6.7 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
  • 7.4 KISS-ICP
  • 7.5 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
  • Suma++
  • Cartographer-KITTI