Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Code exercises for the SLAM courses
| Date | Stars |
|---|---|
| 2026-07-24 | 403 |
| 2026-07-25 | 403 |
| 2026-07-28 | 403 |
| 2026-07-30 | 403 |
| 2026-08-06 | 403 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 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.
Excerpt of 12,843 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:63a081df919fcc41, topic:slam, readme:robotics, readme:autonomous driving