# changh95/visual-slam-roadmap

Roadmap to become a Visual-SLAM developer in 2026

Repository: https://github.com/changh95/visual-slam-roadmap
Canonical: https://ross.abutalabs.com/products/visual-slam-roadmap
Homepage: https://www.cv-learn.com/visual-slam-roadmap/
Language: Astro
License: MIT
License Family: permissive
Topics: visual-slam, slam, roadmap, robotics, computer-vision, awesome, awesome-list, visual-inertial-odometry, vio, rgb-d, deep-learning
Last push: 2026-07-19T23:40:16+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 2052, "days_push": 45, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1763, forks 171 (observed 2026-08-28T04:05:32.843476+00:00)

## What it is
A curated roadmap and study guide for becoming a Visual-SLAM developer, organized into 11 levels with 400+ study notes covering math fundamentals through world models and spatial AI. It is published as a website built with Astro and maintained as an open-source awesome-list-style resource.

## Use cases
- learn visual slam from scratch
- find a study path to become a slam engineer
- find landmark papers and study notes for visual slam
- learn visual-inertial odometry and vio fundamentals
- understand rgb-d and stereo slam concepts
- explore deep learning approaches to slam
- get an overview of lidar and event camera slam

## When to choose
- you are a beginner confused about where to start learning Visual-SLAM
- you want a structured, level-by-level curriculum with curated papers and notes
- you want coverage of modern topics like neural SLAM, multi-robot SLAM, and world models

## When to avoid
- you need a working SLAM library or implementation rather than learning material
- you want an interactive course with graded assignments
- you need non-visual SLAM topics like pure LiDAR-only odometry in depth

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: robotics, computer-vision, tutorials, awesome-lists, artificial-intelligence
- platform: cross-platform
- tags: visual-slam, slam, roadmap, visual-inertial-odometry, study-guide, curriculum, lidar, event-camera, web-server

## Member repositories
- changh95/visual-slam-roadmap (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:32.843476+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:26:45.500015+00:00, confidence not recorded.
  - readme: https://github.com/changh95/visual-slam-roadmap (fetched 2026-08-28T04:05:32.843476+00:00, sha 7fd2c0c50460)
  - homepage: https://www.cv-learn.com/visual-slam-roadmap/ (fetched 2026-08-29T11:05:29.147783+00:00, sha b33599687ef1)
- Data as of 2026-08-30T08:39:29.467469+00:00.