# kanster/awesome-slam

A curated list of awesome SLAM tutorials, projects and communities.

Repository: https://github.com/kanster/awesome-slam
Canonical: https://ross.abutalabs.com/products/awesome-slam
License Family: other
Last push: 2020-07-13T17:49:45+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3755, "days_push": 2242, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1672, forks 376 (observed 2026-08-28T04:05:20.196503+00:00)

## What it is
A curated awesome-list of SLAM (Simultaneous Localization and Mapping) resources including books, courses, papers, datasets, and open-source code. It serves as a reference index for researchers and practitioners in robotics and computer vision.

## Use cases
- find tutorials for learning slam
- discover open-source slam implementations
- find datasets for slam research
- locate courses on robot mapping and state estimation
- find papers on simultaneous localization and mapping

## When to choose
- you are starting to learn SLAM and want a curated entry point
- you need to survey available SLAM datasets, papers, or code
- you are a robotics researcher looking for reference material

## When to avoid
- you need a working SLAM library rather than a list of links
- you need actively maintained or up-to-date content (last updated 2020)
- you need a runnable tool or framework

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: robotics, computer-vision, tutorials, awesome-lists
- platform: cross-platform
- tags: slam, awesome-list, curated-list, state-estimation, mapping, localization

## Member repositories
- kanster/awesome-slam (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:20.196503+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 2026-08-30T03:42:37.589525+00:00, confidence not recorded.
  - readme: https://github.com/kanster/awesome-slam (fetched 2026-08-28T04:05:20.196503+00:00, sha 5c97013a4958)
- Data as of 2026-08-30T08:39:29.467469+00:00.