RRepoGEO

REPOGEO REPORT · LITE

sair-lab/AirSLAM

Default branch master · commit d1265771 · scanned 6/18/2026, 11:51:48 PM

GitHub: 1,169 stars · 173 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)

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

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 sair-lab/AirSLAM, 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
  • highabout#1
    Expand the repository description for better AI indexing

    Why:

    CURRENT
    [TRO 2025] AirVO upgrades to AirSLAM
    COPY-PASTE FIX
    AirSLAM is an efficient and illumination-robust point-line visual SLAM system, accepted to TRO 2025, offering dual-mode V-SLAM and VI-SLAM capabilities as an upgrade from AirVO.
  • mediumtopics#2
    Add more specific and descriptive topics

    Why:

    CURRENT
    odometry, slam, superglue, superpoint, visual-odometry, visual-slam
    COPY-PASTE FIX
    odometry, slam, superglue, superpoint, visual-odometry, visual-slam, vi-slam, point-line-slam, aerial-robotics, uav, drone, illumination-robust
  • lowreadme#3
    Integrate key features into the README's introductory paragraph

    Why:

    CURRENT
    ### Accepted to IEEE Transactions on Robotics (TRO), 2025

### :scroll: AirSLAM has dual-mode (V-SLAM, VI-SLAM), upgraded from AirVO (IROS'23)

<p align="middle">
  
</p>

**AirSLAM** is an efficient
    COPY-PASTE FIX
    AirSLAM, accepted to IEEE Transactions on Robotics (TRO) in 2025, is an efficient and illumination-robust point-line visual SLAM system. Upgraded from AirVO (IROS'23), it features a powerful dual-mode capability, supporting both V-SLAM and VI-SLAM for enhanced performance in challenging environments.

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 sair-lab/AirSLAM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ORB-SLAM3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ORB-SLAM3 · recommended 2×
  2. VINS-Fusion · recommended 2×
  3. DynaSLAM · recommended 1×
  4. OpenVSLAM · recommended 1×
  5. Direct Sparse Odometry (DSO) · recommended 1×
  • CATEGORY QUERY
    What are robust visual SLAM algorithms for accurate localization in varying light?
    you: not recommended
    AI recommended (in order):
    1. ORB-SLAM3
    2. VINS-Fusion
    3. DynaSLAM
    4. OpenVSLAM
    5. Direct Sparse Odometry (DSO)
    6. S-PTAM (Semidense Parallel Tracking and Mapping)

    AI recommended 6 alternatives but never named sair-lab/AirSLAM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Suggest efficient visual SLAM systems that leverage both point and line features.
    you: not recommended
    AI recommended (in order):
    1. PL-SLAM
    2. StructSLAM2
    3. LSD-SLAM
    4. ORB-SLAM3
    5. VINS-Fusion

    AI recommended 5 alternatives but never named sair-lab/AirSLAM. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 sair-lab/AirSLAM?
    pass
    AI named sair-lab/AirSLAM explicitly

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

  • If a team adopts sair-lab/AirSLAM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sair-lab/AirSLAM 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 sair-lab/AirSLAM solve, and who is the primary audience?
    pass
    AI named sair-lab/AirSLAM explicitly

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

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sair-lab/AirSLAM — 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