# tum-vision/lsd_slam

LSD-SLAM

Repository: https://github.com/tum-vision/lsd_slam
Canonical: https://ross.abutalabs.com/products/lsd_slam
Language: C++
License: GPL-3.0
License Family: copyleft
Last push: 2023-03-23T14:55:27+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": 4372, "days_push": 1259, "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 2724, forks 1228 (observed 2026-08-28T04:07:15.716652+00:00)

## What it is
LSD-SLAM is a real-time monocular SLAM system that uses direct (featureless) image alignment to build large-scale, semi-dense 3D maps from a single camera. It is a C++ research codebase built around ROS for input/output, from TUM's computer vision group.

## Use cases
- real-time monocular SLAM from a single camera stream
- build semi-dense 3D point cloud maps from video
- visual odometry without feature extraction
- reconstruct indoor environments from a rosbag
- research baseline for direct SLAM methods

## When to choose
- you need real-time semi-dense monocular SLAM on a laptop
- your scene lacks distinctive keypoints or textures suitable for feature-based SLAM
- you are reproducing or comparing against the LSD-SLAM papers
- your pipeline already uses ROS for camera input and visualization

## When to avoid
- you need a maintained, easily buildable system - it targets old Ubuntu 12.04/14.04 and ROS fuerte/indigo
- you want stereo, RGB-D, or multi-camera SLAM - it is monocular only
- you need dense maps rather than semi-dense ones
- you cannot use ROS, since building without ROS is unsupported

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, graphics, simulation
- domain: robotics, computer-vision, autonomous-vehicles
- platform: cpp
- tags: slam, monocular-slam, visual-odometry, direct-method, semi-dense-mapping, ros, 3d-reconstruction, real-time, linux

## Member repositories
- tum-vision/lsd_slam (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:15.716652+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 2026-08-30T02:14:56.040393+00:00, confidence not recorded.
  - readme: https://github.com/tum-vision/lsd_slam (fetched 2026-08-28T04:07:15.716652+00:00, sha f67a45b84070)
- Data as of 2026-08-30T08:39:29.467469+00:00.