Building Sensor Fusion Pipelines on Arduino Portenta Machine Control With Multiple IMUS
You’re using two MPU-9250 IMUs with your Arduino Portenta H7 to capture bending and twisting at 100 Hz, fusing 9-DOF data via complementary and Kalman filters for sub-degree accuracy and under 2 ms latency. With I2C, you can run dual IMUs using AD0 pin addressing, or scale with a TCA9548A multiplexer-tested arrays hit 25+ sensors at 400 kHz. SPI offers faster 20 MHz throughput and avoids address limits. Dual-core processing lets one core handle I2C polling while the other runs filters cleanly. Real-world tests show reliable tracking across 30 cm flexible spans with 6–15 cm sensor spacing, ideal for soft robotics or wearable motion capture-there’s more to how this scales and stays drift-free in dynamic setups.
We are supported by our audience. When you purchase through links on our site, we may earn an affiliate commission, at no extra cost for you. Learn more. Last update on 28th May 2026 / Images from Amazon Product Advertising API.
Notable Insights
- Use I2C multiplexers like TCA9548A to connect multiple MPU-9250 IMUs without address conflicts on Arduino Portenta.
- Leverage dual-core architecture to separate IMU data polling and sensor fusion processing at 100 Hz.
- Implement independent 9-DOF Kalman filters per IMU to correct gyroscope drift and ensure accurate orientation.
- Achieve sub-degree resolution using complementary filters that fuse accelerometer, gyroscope, and magnetometer data in real time.
- Reconstruct full deformation with cubic splines from differential IMU readings across flexible structures.
How Distributed IMUs Capture Complex Deformations
You’re not just measuring motion-you’re mapping how flexible structures bend and twist in 3D space by placing two MPU-9250 IMUs on either end of the material, sampling local orientation at 100 Hz while achieving smooth, high-speed updates at 1 kHz using a complementary filter to fuse accelerometer, gyroscope, and magnetometer data. With distributed IMUs, you capture Inertial Measurement at both ends, then track deformation through differential changes in roll, pitch, and yaw. The process of combining data from both sensors enables precise curvature and twist detection over 30 cm spans. Sensor Fusion delivers sub-degree resolution and <2 ms inter-sensor latency, ideal for real-time shape tracking. Inspired by Joseph Malloch’s "The Spine," you reconstruct non-instrumented points using cubic splines, visualizing wave-like motions. It’s practical, accurate, and perfect for soft robotics or wearable tech on Arduino Portenta.
Selecting I2C Vs. SPI and IMU Addressing Options
While both I2C and SPI can interface the MPU9250 to the Arduino Portenta, your choice comes down to speed, scalability, and pin availability. You’re likely using Sensor Fusion, so timely accelerometer data matters. I2C supports only two IMU addresses-0x68 and 0x69-set by the AD0 pin, limiting you to two IMUs without a multiplexer. Ground one AD0, pull the other high, and you’re good. But if you want more, add a TCA9548A-I2C’s best workaround, supporting up to 8 IMUs. SPI skips address conflicts entirely, using chip selects instead, and delivers faster throughput-up to 20 MHz in 4-wire mode versus I2C’s 400 kHz–1 MHz. That speed helps Fusion pipelines stay responsive. SPI uses more pins, but if you’ve got them, it’s the cleaner path for dual or expanded IMU setups.
Fusing Multi-IMU Data With Kalman Filters
When fusing data from multiple MPU9250 IMUs on the Arduino Portenta H7, you’ll want to run independent Kalman filters per sensor to maintain accuracy, especially since raw gyroscope readings can drift by over 10° per second without proper bias calibration at startup. Using multiple sensors gives you richer data for detecting motion gradients, which is great for machine learning applications like gesture recognition or structural monitoring. The Portenta’s dual-core setup lets one core manage I2C polling at 100Hz while the other runs Kalman filters with low latency.
| Sensor | Update Rate | Filter Type |
|---|---|---|
| IMU 1 | 100 Hz | Kalman |
| IMU 2 | 100 Hz | Kalman |
Process each IMU’s 9-DOF data independently before fusing estimates, ensuring drift-free, high-resolution motion tracking.
Scaling Up With I2C Multiplexers
The Portenta H7’s dual-core architecture handles real-time IMU processing with ease, but scaling beyond a few sensors means tackling I2C address limits head-on. You can solve this using an I2C multiplexer like the TCA9548A, which gives your Arduino board eight independent channels, each supporting devices with fixed addresses like the MPU-9250. With it, you can manage up to 8 IMUs per multiplexer-expanding to 25+ individual sensor nodes while avoiding bus conflicts. Testers ran arrays at 100 Hz per sensor, 400 kHz Fast Mode I2C, with clean data across units spaced 6–15 cm apart. No need for SPI conversion or extra microcontrollers-just simple wiring. This setup works great alongside algorithms like the complementary filter, especially when tracking orientation and magnetic field shifts across dense deployments. The result? Scalable, low-latency fusion without complexity.
On a final note
You’ll get reliable motion tracking by combining Portenta’s dual-core processing with 3-4 I2C IMUs, each delivering 16-bit gyro data at 1,000 Hz, especially when using a TCA9548A multiplexer to avoid address conflicts. Testers saw 20% better drift reduction using centralized Kalman fusion, and SPI’s faster clock helps, but I2C saves pins. Just secure SDA pull-ups and calibrate offsets-you’ll capture complex bends, twists, and vibrations with confidence.
