# lukasvst/dm-vio

Source code for the paper DM-VIO: Delayed Marginalization Visual-Inertial Odometry

Repository: https://github.com/lukasvst/dm-vio
Canonical: https://ross.abutalabs.com/products/dm-vio
Language: C++
License: GPL-3.0
License Family: copyleft
Last push: 2024-10-27T02:14:34+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": 1695, "days_push": 676, "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 1224, forks 210 (observed 2026-08-28T04:04:02.899573+00:00)

## What it is
DM-VIO is a C++ implementation of Delayed Marginalization Visual-Inertial Odometry, a direct sparse visual-inertial odometry method published in IEEE RA-L 2022. It estimates camera and IMU trajectory from monocular video and IMU data, with a ROS wrapper and live Realsense camera support.

## Use cases
- estimate camera trajectory from monocular video and IMU data
- run visual-inertial odometry on EuRoC or TUM-VI datasets
- track a Realsense camera in real time
- process rosbags offline with a ROS wrapper
- reproduce results from the DM-VIO research paper
- build SLAM or drone navigation on top of a VIO backend

## When to choose
- you need accurate monocular VIO with IMU, especially with scale drift handling
- you want a research-grade odometry system with dataset evaluation support
- you use Realsense cameras or ROS and want a ready-made wrapper

## When to avoid
- you need a full SLAM system with loop closure and mapping
- you want RGB-D or stereo odometry out of the box
- you need a plug-and-play solution without building GTSAM and C++ dependencies from source

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, simulation, math
- domain: robotics, computer-vision, autonomous-vehicles
- platform: cpp
- tags: visual-inertial-odometry, slam, state-estimation, gtsam, research-code, camera-tracking, imu, linux, macos

## Member repositories
- lukasvst/dm-vio (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:02.899573+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 2026-08-30T06:15:28.375094+00:00, confidence not recorded.
  - readme: https://github.com/lukasvst/dm-vio (fetched 2026-08-28T04:04:02.899573+00:00, sha f204277b820c)
- Data as of 2026-08-30T08:39:29.467469+00:00.