# princeton-vl/DROID-SLAM

Repository: https://github.com/princeton-vl/DROID-SLAM
Canonical: https://ross.abutalabs.com/products/droid-slam
Language: Python
License: BSD-3-Clause
License Family: permissive
Last push: 2025-05-05T05:29:42+00:00

## Health v2 (maintenance only)
Score: 41/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 20, release rhythm 35, longevity 100
- inputs: {"age_days": 1835, "days_push": 485, "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 2671, forks 427 (observed 2026-08-28T04:07:09.522910+00:00)

## What it is
DROID-SLAM is a deep learning-based visual SLAM system for monocular, stereo, and RGB-D cameras that estimates camera trajectory and dense 3D reconstruction. It is a PyTorch/CUDA research library from Princeton Vision Lab requiring a GPU for inference.

## Use cases
- estimate camera trajectory from monocular video
- run visual SLAM with stereo or RGB-D cameras
- reconstruct dense 3D depth maps from video
- benchmark SLAM on standard datasets
- run SLAM inference on multiple GPUs

## When to choose
- you need deep-learning-based SLAM with strong accuracy on monocular, stereo, or RGB-D input
- you have a CUDA GPU with at least 11GB memory and a PyTorch workflow
- you want dense depth reconstructions alongside camera poses

## When to avoid
- you need lightweight real-time SLAM on CPU or embedded hardware without a GPU
- you need a production-ready plug-and-play SLAM solution rather than research code
- you lack CUDA-compatible hardware

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning, simulation
- domain: computer-vision, robotics, autonomous-vehicles, deep-learning
- platform: python, cpp
- tags: slam, visual-odometry, monocular-depth, stereo, rgbd, pytorch, camera-pose-estimation, 3d-reconstruction, gpu, linux

## Member repositories
- princeton-vl/DROID-SLAM (main) score 41

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:09.522910+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 2026-08-30T02:18:09.097884+00:00, confidence not recorded.
  - readme: https://github.com/princeton-vl/DROID-SLAM (fetched 2026-08-28T04:07:09.522910+00:00, sha 91a6ab71a8cf)
- Data as of 2026-08-30T08:39:29.467469+00:00.