# youngguncho/awesome-slam-datasets

A curated list of awesome datasets for SLAM

Repository: https://github.com/youngguncho/awesome-slam-datasets
Canonical: https://ross.abutalabs.com/products/awesome-slam-datasets
License Family: other
Last push: 2024-12-13T04:29:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3034, "days_push": 628, "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 1944, forks 347 (observed 2026-08-28T04:05:57.647062+00:00)

## What it is
A curated awesome-list of datasets for SLAM (Simultaneous Localization and Mapping), collecting datasets that provide pose and map ground truth. It covers visual, LiDAR, RGBD, IMU, and event-camera datasets for robotics and autonomous driving research, plus evaluation tools.

## Use cases
- find SLAM datasets with ground truth poses
- benchmark visual odometry algorithms
- locate LiDAR datasets for mapping research
- find RGBD datasets for dense reconstruction
- compare drone and autonomous driving datasets
- find evaluation tools for SLAM trajectories

## When to choose
- researching or benchmarking SLAM, VIO, or odometry algorithms
- looking for datasets with pose and map ground truth
- surveying available robotics perception datasets

## When to avoid
- you need SLAM algorithms or implementations rather than data
- you need datasets without pose/map annotations
- you need a maintained software tool rather than a link list

## Facets
- artifact type: dataset
- maturity: active
- function: developer-tools
- domain: robotics, autonomous-vehicles, computer-vision, awesome-lists
- platform: cross-platform
- tags: slam, datasets, curated-list, benchmarking, lidar, visual-odometry, dataset

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:57.647062+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 2026-08-30T03:08:01.041750+00:00, confidence not recorded.
  - readme: https://github.com/youngguncho/awesome-slam-datasets (fetched 2026-08-28T04:05:57.647062+00:00, sha 36fadfe77108)
- Data as of 2026-08-30T08:39:29.467469+00:00.