RRepoGEO

REPOGEO REPORT · LITE

changh95/visual-slam-roadmap

Default branch main · commit d28b99c5 · scanned 6/25/2026, 8:08:00 AM

GitHub: 1,705 stars · 164 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface changh95/visual-slam-roadmap, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening paragraph to emphasize its role as a learning roadmap

    Why:

    CURRENT
    Visual-SLAM is a special case of 'Simultaneous Localization and Mapping', which you use a camera device to gather exteroceptive sensory data. Below there is a set of topics you need to understand in Visual-SLAM, from an absolute beginner difficulty to getting ready to become a Visual-SLAM engineer / researcher.
    COPY-PASTE FIX
    This repository presents the Visual-SLAM Developer Roadmap for 2026, a comprehensive, step-by-step learning path for aspiring Visual-SLAM engineers and researchers. Unlike individual libraries or academic texts, this roadmap curates and organizes all essential topics, guiding you from beginner to proficiency in Visual-SLAM.
  • mediumhomepage#2
    Add the repository URL as the homepage link in the About section

    Why:

    COPY-PASTE FIX
    https://github.com/changh95/visual-slam-roadmap
  • lowreadme#3
    Add a small section to the README highlighting the roadmap's core differentiator

    Why:

    COPY-PASTE FIX
    ### How this Roadmap Differs from Other Resources
Unlike individual Visual SLAM libraries (e.g., ORB-SLAM3, LSD-SLAM) or general computer vision tools (e.g., OpenCV, Ceres Solver), this repository provides a curated, step-by-step learning roadmap. Its core differentiator is organizing diverse resources (papers, books, courses, code) into a logical progression specifically designed for learners to become proficient Visual SLAM developers.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface changh95/visual-slam-roadmap
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenCV
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenCV · recommended 2×
  2. ORB-SLAM3 · recommended 2×
  3. LSD-SLAM · recommended 2×
  4. Ceres Solver · recommended 1×
  5. g2o · recommended 1×
  • CATEGORY QUERY
    What's a good learning path to become proficient in visual simultaneous localization and mapping?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. Ceres Solver
    3. g2o
    4. DBoW3
    5. PCL (Point Cloud Library)
    6. ORB-SLAM3
    7. VINS-Mono
    8. VINS-Fusion
    9. LSD-SLAM
    10. DSO (Direct Sparse Odometry)
    11. RTAB-Map (Real-Time Appearance-Based Mapping)
    12. C++
    13. Python
    14. CLion
    15. VS Code
    16. CMake
    17. Ubuntu (Linux)
    18. ROS (Robotics Operating System)

    AI recommended 18 alternatives but never named changh95/visual-slam-roadmap. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive guide for starting a career in visual SLAM?
    you: not recommended
    AI recommended (in order):
    1. Multiple View Geometry in Computer Vision
    2. Probabilistic Robotics
    3. OpenCV
    4. Medium
    5. Towards Data Science
    6. ORB-SLAM
    7. LSD-SLAM
    8. RTAB-Map
    9. ORB-SLAM3
    10. Coursera
    11. Udacity
    12. edX

    AI recommended 12 alternatives but never named changh95/visual-slam-roadmap. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of changh95/visual-slam-roadmap?
    pass
    AI did not name changh95/visual-slam-roadmap — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts changh95/visual-slam-roadmap in production, what risks or prerequisites should they evaluate first?
    pass
    AI named changh95/visual-slam-roadmap explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo changh95/visual-slam-roadmap solve, and who is the primary audience?
    pass
    AI named changh95/visual-slam-roadmap explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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changh95/visual-slam-roadmap — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite