RRepoGEO

REPOGEO REPORT · LITE

changh95/visual-slam-roadmap

Default branch main · commit d28b99c5 · scanned 5/14/2026, 6:28:41 PM

GitHub: 1,679 stars · 162 forks

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 clarify its format as a resource list

    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 provides a comprehensive, curated roadmap and awesome list of resources for aspiring Visual-SLAM developers, guiding you from beginner concepts to advanced engineering and research topics.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/changh95/visual-slam-roadmap
  • mediumtopics#3
    Add more descriptive topics to reinforce its nature as a learning resource

    Why:

    CURRENT
    awesome, awesome-list, computer-vision, deep-learning, rgb-d, roadmap, robotics, slam, vio, visual-inertial-odometry, visual-slam
    COPY-PASTE FIX
    awesome, awesome-list, computer-vision, deep-learning, rgb-d, roadmap, robotics, slam, vio, visual-inertial-odometry, visual-slam, learning-path, learning-guide, educational-resources

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
SLAM for Dummies
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. SLAM for Dummies · recommended 1×
  2. Probabilistic Robotics · recommended 1×
  3. Multiple View Geometry in Computer Vision · recommended 1×
  4. OpenCV · recommended 1×
  5. ORB-SLAM3 · recommended 1×
  • CATEGORY QUERY
    What is a good learning path to become a Visual-SLAM engineer?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive roadmap for learning visual simultaneous localization and mapping?
    you: not recommended
    AI recommended (in order):
    1. SLAM for Dummies
    2. Probabilistic Robotics
    3. Multiple View Geometry in Computer Vision
    4. OpenCV
    5. ORB-SLAM3
    6. Visual SLAM: A Comprehensive Tutorial from Theory to Practice
    7. ROS
    8. RTAB-Map
    9. VINS-Fusion

    AI recommended 9 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