ethz-asl/rovio
None observed · 2026-08-28
Health v2 · maintenance only
61/100
- Activity 63
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4045
- days_rel: n/a
- days_push: 226
- n_releases_24m: 0
Adoption not part of the score
1262 stars · 520 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
ROVIO (Robust Visual Inertial Odometry) is a C++ framework from ETH Zurich that estimates camera and IMU trajectory using an iterated extended Kalman filter with patch-based feature tracking. It is built as a ROS package and is tightly coupled to ongoing research.
Use cases
- estimate camera trajectory from camera and IMU data
- visual inertial odometry for drones
- run odometry on Euroc MAV datasets
- track pose of a robot in real time
- online IMU-camera extrinsic calibration
When to choose
- you need robust, real-time visual-inertial odometry in a ROS pipeline
- you work with camera+IMU setups such as drones or MAVs
- you want a research-proven odometry framework with published papers
When to avoid
- you need a fully mature, stable production SLAM system
- you are not using ROS or C++
- you need full SLAM with loop closure and mapping rather than odometry
Facets
library · maturity active
computer-vision robotics simulation robotics computer-vision autonomous-vehicles cpp visual-inertial-odometry slam state-estimation kalman-filter ros eth-zurich linux
1 source
- readme: https://github.com/ethz-asl/rovio · fetched 2026-08-28 · e7b7e8718a5f
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ethz-asl/rovio | main | 61 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem