est. 2021 · rev. 2026

Visual-SLAM
Developer Roadmap

A guided path from camera basics to world models — 11 levels, 400+ study notes covering the theory, the landmark papers with their actual equations and results, and hands-on code exercises.

// keyframes 01–11

  1. 0116 notes

    Beginner

    Math, programming, and camera/image fundamentals

  2. 0278 notes

    Getting Familiar with SLAM

    Geometry, optimization, and the anatomy of a SLAM system

  3. 0340 notes

    Monocular Visual-SLAM

    Classical monocular SLAM — feature-based, direct, semi-direct, SfM, dynamic scenes

  4. 0423 notes

    RGB-D Visual-SLAM

    Dense tracking and volumetric/surfel fusion with depth sensors

  5. 05126 notes

    Applying Deep Learning

    Learned frontends, differentiable backends, end-to-end systems, foundation-model & neural SLAM, scene understanding

  6. 0623 notes

    VIO / VINS

    Fusing cameras with IMUs — filtering vs optimization

  7. 076 notes

    Stereo SLAM

    Metric scale and depth from stereo pairs

  8. 0811 notes

    Collaborative / Multi-Robot SLAM

    Multi-robot mapping, inter-robot loop closure, map merging

  9. 0917 notes

    LiDAR & Visual-LiDAR Fusion SLAM

    LiDAR odometry and tight camera–LiDAR–IMU fusion

  10. 1013 notes

    Event Camera SLAM

    Asynchronous vision for HDR and high-speed motion

  11. 1117 notes

    World Models & Spatial AI

    From SLAM maps to learned world representations

  12. +

    Study Resources

    Lectures, books, surveys, and code exercises