# zdzhaoyong/GSLAM

A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.

Repository: https://github.com/zdzhaoyong/GSLAM
Canonical: https://ross.abutalabs.com/products/gslam
Language: C++
License: BSD-2-Clause
License Family: permissive
Last push: 2020-04-01T15:30:20+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3675, "days_push": 2345, "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 1058, forks 270 (observed 2026-08-28T04:03:25.178259+00:00)

## What it is
GSLAM is a general Simultaneous Localization and Mapping (SLAM) framework and benchmark written in C++. It provides a unified plugin-based API so SLAM implementations (feature-based or direct method, using monocular, RGB-D, or other sensors) can be loaded at runtime without recompilation.

## Use cases
- build a custom SLAM system on a common framework
- benchmark and compare different SLAM algorithms
- run SLAM with monocular or RGB-D camera input
- load SLAM implementations as runtime plugins
- evaluate SLAM pipelines on standard datasets
- develop robotics localization and mapping research prototypes

## When to choose
- you need a unified API to swap between multiple SLAM implementations
- you want to benchmark SLAM algorithms on datasets
- you are developing a SLAM system and want plugin-based distribution
- you work with heterogeneous sensors like monocular or RGB-D cameras

## When to avoid
- you need a production-ready, actively maintained SLAM solution with recent updates
- you want a turnkey SLAM application rather than a development framework
- your project requires a license or ecosystem outside BSD-2-Clause C++ tooling
- you need deep learning based SLAM out of the box

## Facets
- artifact type: framework
- maturity: maintenance
- function: simulation, computer-vision, plugin-system, middleware, benchmarking
- domain: robotics, computer-vision, simulation, developer-tools
- platform: cpp, cross-platform
- tags: slam, visual-odometry, rgb-d, monocular-camera, mapping, localization, benchmark, runtime-plugins, linux

## Member repositories
- zdzhaoyong/GSLAM (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:25.178259+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 2026-08-30T06:57:53.487684+00:00, confidence not recorded.
  - readme: https://github.com/zdzhaoyong/GSLAM (fetched 2026-08-28T04:03:25.178259+00:00, sha f2c63352fd59)
- Data as of 2026-08-30T08:39:29.467469+00:00.