# kriswiner/MPU9250

Arduino sketches for MPU9250 9DoF with AHRS sensor fusion

Repository: https://github.com/kriswiner/MPU9250
Canonical: https://ross.abutalabs.com/products/mpu9250
Language: C++
License Family: other
Topics: mpu9250, sensor-fusion
Last push: 2019-05-11T19:28:04+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": 4500, "days_push": 2671, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1088, forks 461 (observed 2026-08-28T04:03:32.469094+00:00)

## What it is
Arduino sketches and supporting code for the MPU-9250 9-DoF motion sensor, including calibration, self-test, and AHRS sensor fusion using Madgwick and Mahony filters. Includes variants for Teensy 3.1 and STM32 Nucleo (mbed) boards.

## Use cases
- read accelerometer gyroscope and magnetometer data from MPU-9250 on Arduino
- implement AHRS attitude estimation with Madgwick or Mahony filter
- calibrate and self-test a 9DoF IMU
- run sensor fusion on Teensy 3.1 or STM32 Nucleo
- build orientation tracking for wearables or drones

## When to choose
- you use an MPU-9250 with Arduino, Teensy, or STM32 and want proven fusion sketches
- you need well-documented calibration and quaternion filter code for 9DoF sensing

## When to avoid
- you need a maintained library with a license for commercial use (no license is provided)
- you use a different IMU chip
- you need a modern build system rather than Arduino IDE sketches

## Facets
- artifact type: library
- maturity: maintenance
- function: embedded, simulation, monitoring
- domain: embedded-systems, hardware, robotics
- platform: embedded, iot, cpp
- tags: mpu9250, sensor-fusion, ahrs, madgwick-filter, mahony-filter, arduino, teensy, imu, 9dof, magnetometer, accelerometer, gyroscope

## Member repositories
- kriswiner/MPU9250 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:32.469094+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 2026-08-30T06:49:22.210551+00:00, confidence not recorded.
  - readme: https://github.com/kriswiner/MPU9250 (fetched 2026-08-28T04:03:32.469094+00:00, sha 0c777f154b91)
- Data as of 2026-08-30T08:39:29.467469+00:00.