REPOGEO REPORT · LITE

luigifreda/pyslam

Default branch master · commit a95ff39d · scanned 5/20/2026, 3:03:14 PM

GitHub: 3,302 stars · 522 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

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 luigifreda/pyslam, 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 README H1 and opening sentence to clarify purpose

Why:

CURRENT

# pySLAM v2.10.5

Author: **Luigi FredapySLAM** is a hybrid **python/C++** implementation of a *Visual SLAM* pipeline (Simultaneous Localization And Mapping) that supports **monocular**, **stereo** and **RGBD** cameras.

COPY-PASTE FIX

# pySLAM v2.10.5: A Python/C++ Visual SLAM Prototyping & Research Toolbox

Author: **Luigi Freda**

pySLAM is a hybrid Python/C++ Visual SLAM pipeline designed as a modular toolbox for researchers and developers to prototype and experiment with monocular, stereo, and RGB-D camera setups.

mediumhomepage#2

Add a project homepage URL

Why:

COPY-PASTE FIX

Add the official project homepage URL (e.g., https://luigifreda.github.io/pyslam-docs)

lowtopics#3

Refine topics to emphasize research and prototyping

Why:

CURRENT

3d-reconstruction, depth-estimation, depth-prediction, end-to-end-reconstruction, feature-matching, gaussian-splatting, global-features, instance-segmentation, local-features, loop-closure, place-recognition, rgbd-slam, scene-understanding, semantic-mapping, semantic-segmentation, semantic-understanding, slam, stereo-slam, visual-odometry, volumetric-reconstruction

COPY-PASTE FIX

3d-reconstruction, depth-estimation, depth-prediction, end-to-end-reconstruction, feature-matching, gaussian-splatting, global-features, instance-segmentation, local-features, loop-closure, place-recognition, rgbd-slam, scene-understanding, semantic-mapping, semantic-segmentation, semantic-understanding, slam, stereo-slam, visual-odometry, volumetric-reconstruction, slam-research, robotics-prototyping, computer-vision-toolbox

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 luigifreda/pyslam

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ORB-SLAM3

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ORB-SLAM3 · recommended 2×
OpenVSLAM · recommended 2×
RTAB-Map · recommended 2×
COLMAP · recommended 1×
Pangolin · recommended 1×

CATEGORY QUERY
What are good Python/C++ libraries for visual SLAM with monocular and stereo camera support?
you: not recommended
AI recommended (in order):
1. ORB-SLAM3
2. OpenVSLAM
3. RTAB-Map
4. COLMAP
5. Pangolin
6. OpenCV
AI recommended 6 alternatives but never named luigifreda/pyslam. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a robust visual SLAM pipeline for 3D reconstruction and semantic scene understanding.
you: not recommended
AI recommended (in order):
1. ORB-SLAM3
2. OpenVSLAM
3. RTAB-Map
4. ElasticFusion
5. VINS-Fusion
6. Kimera-VIO
7. Google Cartographer
AI recommended 7 alternatives but never named luigifreda/pyslam. 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 luigifreda/pyslam?
pass
AI did not name luigifreda/pyslam — 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 luigifreda/pyslam in production, what risks or prerequisites should they evaluate first?
pass
AI named luigifreda/pyslam 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 luigifreda/pyslam solve, and who is the primary audience?
pass
AI named luigifreda/pyslam explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of luigifreda/pyslam. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/luigifreda/pyslam.svg)](https://repogeo.com/en/r/luigifreda/pyslam)

HTML

<a href="https://repogeo.com/en/r/luigifreda/pyslam"><img src="https://repogeo.com/badge/luigifreda/pyslam.svg" alt="RepoGEO" /></a>

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luigifreda/pyslam — 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