# sair-lab/AirSLAM

[TRO 2025] AirVO upgrades to AirSLAM

Repository: https://github.com/sair-lab/AirSLAM
Canonical: https://ross.abutalabs.com/products/airslam
Homepage: https://sairlab.org/airslam/
Language: C++
License: GPL-3.0
License Family: copyleft
Topics: odometry, slam, visual-odometry, superpoint, superglue, visual-slam
Last push: 2025-11-19T03:18:53+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 53, release rhythm 8, longevity 100
- inputs: {"age_days": 1569, "days_push": 287, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1190, forks 176 (observed 2026-08-28T04:03:55.962765+00:00)

## What it is
AirSLAM is an efficient, illumination-robust point-line visual SLAM system supporting stereo visual odometry/VIO, offline map optimization, and lightweight relocalization. It combines a unified CNN for keypoint and line detection (accelerated with C++ and NVIDIA TensorRT) with traditional geometric backend optimization.

## Use cases
- run visual SLAM in illumination-challenging environments
- estimate camera trajectory from stereo or stereo-inertial video
- relocalize a robot against a previously built map
- deploy real-time SLAM on embedded platforms like Jetson
- build and optimize point-line maps offline
- research deep-learned feature detection and matching for SLAM

## When to choose
- you need robust visual odometry/SLAM under lighting changes
- you want GPU-accelerated SuperPoint/SuperGlue-style features in C++ with TensorRT
- you need high frame rates (40-73Hz) on PC or embedded hardware
- you need both V-SLAM and VI-SLAM modes with relocalization

## When to avoid
- you need LiDAR or RGB-D based SLAM
- you need a pure CPU solution without NVIDIA GPUs
- you need a plug-and-play library with a stable API rather than a research system
- your project is incompatible with the GPL-3.0 license

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, simulation
- domain: robotics, computer-vision, autonomous-vehicles
- platform: cpp
- tags: visual-slam, visual-odometry, superpoint, superglue, tensorrt, relocalization, point-line-features, vslam, vio, linux, gpu

## Member repositories
- sair-lab/AirSLAM (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:55.962765+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 2026-08-30T06:23:05.673803+00:00, confidence not recorded.
  - readme: https://github.com/sair-lab/AirSLAM (fetched 2026-08-28T04:03:55.962765+00:00, sha 6f84c1860a43)
  - homepage: https://sairlab.org/airslam/ (fetched 2026-08-29T12:30:07.051037+00:00, sha 1b4fbe075dfe)
- Data as of 2026-08-30T08:39:29.467469+00:00.