# geohot/twitchslam

A toy implementation of monocular SLAM written while livestreaming

Repository: https://github.com/geohot/twitchslam
Canonical: https://ross.abutalabs.com/products/twitchslam
Language: Python
License: MIT
License Family: permissive
Last push: 2022-05-16T04:59:20+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": 3020, "days_push": 1570, "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 1003, forks 212 (observed 2026-09-03T02:15:12.277126+00:00)

## What it is
A toy monocular SLAM (Simultaneous Localization and Mapping) implementation written in Python during livestreams. It extracts features from video frames, builds a 3D point map, and estimates camera pose using OpenCV, SDL2, Pangolin, and g2o bindings.

## Use cases
- learn how monocular SLAM works from readable code
- run visual odometry on a video file
- experiment with feature extraction and pose estimation
- test SLAM against KITTI or TUM freiburg datasets
- prototype a simple 3D map from camera video

## When to choose
- you want a small, hackable SLAM codebase to study or modify
- you're learning computer vision concepts like essential matrices and bundle adjustment
- you need a starting point for a custom visual SLAM experiment

## When to avoid
- you need a production-grade or accurate SLAM system
- you require robustness, automated tests, or long-term maintenance
- you need stereo, RGB-D, or real-time performance guarantees

## Facets
- artifact type: library
- maturity: experimental
- function: computer-vision, image-processing, graphics, simulation
- domain: computer-vision, robotics, education
- platform: python
- tags: slam, monocular-slam, livestream-project, feature-tracking, pose-estimation, toy-implementation, algorithms, linux, desktop

## Member repositories
- geohot/twitchslam (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:12.277126+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 2026-08-30T07:13:59.950157+00:00, confidence not recorded.
  - readme: https://github.com/geohot/twitchslam (fetched 2026-09-03T02:15:12.277126+00:00, sha e7f5756c77bb)
- Data as of 2026-08-30T08:39:29.467469+00:00.