How to reduce latency and increase sample rate of MPU9250 with Python on Raspberry Pi?

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#1 21681620 22 Jan 2019 14:59

John Manuel John Manuel

Anonymous

Post #1
21681620 22 Jan 2019 14:59

Hi, I am using MPU9250 with Raspberry Pi and coding with Python. I am trying to make a virtual compass. The compass is working fine. But when I move the compass swiftly, it takes some time (around 2 seconds) to stabilize to the new direction. MPU9250 records a few (around 5) data points during the interval but it takes time to output it. Is there any way of reducing this latency and increasing the number of data points so that the virtual compass will follow the real motion more simultaneously? I have been using the following Python library - Link. I have tried using "Accel Full Scale Select 16G" and "Gyro Full Scale Select 2000dps" but there was no observable difference.
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#2 21681621 22 Jan 2019 23:07

Elizabeth Simon Elizabeth Simon

Anonymous

Post #2
21681621 22 Jan 2019 23:07

John,
I suspect that you are going to have to dig a bit deeper into how the MPU9250 actually works. I found a link to the part from the manufacturer. You should be able to get to the datasheet from there.
https://www.invensense.com/products/motion-tracking/9-axis/mpu-9250/
I took a peek and it seems that the magnetometer (compass) has a much slower update than the accelerometer and gyroscope. But it should still be faster than what you were seeing. Unless the library you are using is relying on the MPU9250 for pre-processing. It sounds like you are going to have to dig into the library.
Elizabeth
#3 21681622 24 Jan 2019 16:00

Garcia Jones Garcia Jones

Anonymous

Post #3
21681622 24 Jan 2019 16:00

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#5 21681624 25 Jan 2019 10:46

john devid john devid

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21681624 25 Jan 2019 10:46

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#6 21681625 03 May 2019 15:58

Hussain Deewan Hussain Deewan

Anonymous

Post #6
21681625 03 May 2019 15:58

I don't think that the latency is that bad, besides if you really wanna make it fast, you will have to dig into the complexity of MPU9250. Also, have you tried other python libraries apart from these two? You can take help from this IT company
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Topic summary

✨ The discussion addresses reducing latency and increasing the sample rate of the MPU9250 sensor when interfaced with a Raspberry Pi using Python for a virtual compass application. The MPU9250's magnetometer update rate is slower compared to its accelerometer and gyroscope, which may contribute to the observed delay in compass stabilization during swift movements. Adjusting sensor full-scale ranges (e.g., accelerometer to 16G and gyroscope to 2000 dps) showed no significant improvement. It is suggested to review the Python library implementation for possible pre-processing delays and consider exploring alternative libraries. Understanding the MPU9250 datasheet and internal operation is recommended to optimize data acquisition rates and reduce latency. No direct software or hardware solutions were confirmed, but deeper investigation into sensor configuration and data handling is necessary.

FAQ

TL;DR: To fix “compass lag,” run the AK8963 magnetometer at 100 Hz—"Continuous measurement mode 2 is 100 Hz"—and pair it with faster reads and sane filtering. [AK8963 Datasheet, 2013]

Why it matters: This FAQ helps Raspberry Pi + MPU-9250 Python users cut ~2 s heading lag and boost sample density for smoother virtual-compass motion.

Quick Facts

Magnetometer (AK8963) supports Continuous 8 Hz (CONT1) and 100 Hz (CONT2) modes; default is often slower. [AK8963 Datasheet, 2013]
Gyro/accel output data rate can be 1 kHz with DLPF enabled; 8 kHz/32 kHz in bypass modes. [MPU-9250 Product Spec, 2014]
Raspberry Pi I2C can be set to 400 kHz via config.txt (i2c_arm_baudrate). [Raspberry Pi Docs, 2025]
DLPF choice changes bandwidth and group delay; lower bandwidth = more latency. [MPU-9250 Product Spec, 2014]
Edge case: switching AK8963 modes requires a power-down then ≥100 µs wait before new mode. [AK8963 Datasheet, 2013]

Why does my MPU-9250 compass feel 1–2 seconds behind?

Because the magnetometer updates far slower than the gyro/accelerometer, and your library may add extra processing. Use the magnetometer’s 100 Hz mode, reduce filter delay, and avoid blocking reads. “The magnetometer has a much slower update.” [Elektroda, Anonymous, post #21681621]

What sample rates can I actually get from the MPU-9250 gyro/accelerometer?

With DLPF enabled, the primary ODR is 1 kHz. In bypass modes, internal rates reach 8 kHz (gyro) and 32 kHz (temperature paths). Use SMPLRT_DIV to downsample. Lower DLPF bandwidth raises bandwidth but can increase noise. [MPU-9250 Product Spec, 2014]

What are the AK8963 magnetometer modes and rates?

AK8963 offers Single, Continuous 1 (8 Hz), and Continuous 2 (100 Hz). For a responsive compass, use Continuous 2 at 100 Hz. Quote: “Sensor is measured periodically in 100 Hz.” [AK8963 Datasheet, 2013]

How do I set the AK8963 to 100 Hz in Python?

Write the AK8963 CNTL1 MODE bits to Continuous 2 after a power-down transition. How-To: 1. Write MODE=0000 (power-down), wait ≥100 µs. 2. Configure resolution (14/16-bit) as needed. 3. Write MODE=0110 (CONT2, 100 Hz) and poll DRDY/INT. [AK8963 Datasheet, 2013]

How can I speed up I2C reads on Raspberry Pi?

Increase bus speed to 400 kHz. Edit /boot/config.txt and set dtparam=i2c_arm=on,i2c_arm_baudrate=400000, then reboot. Faster bus = lower transfer latency for each sensor read. [Raspberry Pi Docs, 2025]

What is I2C clock stretching, and why can it cause lag on Pi?

Some sensors hold the clock low to delay transfers (clock stretching). Raspberry Pi’s hardware I2C doesn’t truly support it, so mismatches can corrupt or slow reads. Workarounds include changing baudrate (slower or faster) until stable. [Adafruit Learning System, 2024]

Does the DLPF add latency to heading updates?

Yes. Narrower DLPF settings increase group delay and smooth noise, adding noticeable lag. Use a higher bandwidth (e.g., 92–184 Hz) for faster response, then filter in software if needed. [MPU-9250 Product Spec, 2014]

Should I read data with interrupts or polling to cut latency?

Use the MPU-9250 INT pin and read immediately on data-ready. Interrupt-driven reads reduce jitter and missed samples compared to slow polling loops. The spec defines INT timing and polarity for reliable triggering. [MPU-9250 Product Spec, 2014]

My library changed accel/gyro full-scale to 16 g and 2000 dps, but latency didn’t improve—why?

Full-scale ranges set dynamic range, not update timing. Latency mostly depends on sensor ODR, DLPF bandwidth, magnetometer mode, and host transfer speed. Adjust those instead for perceptible gains. [MPU-9250 Product Spec, 2014]

Could my Python library be adding delay?

Yes. Extra fusion, averaging, or sleep calls add latency. One reply notes the library might rely on built-in preprocessing; inspect and trim filters or use non-blocking I/O. [Elektroda, Anonymous, post #21681621]

What simple 3-step plan will cut my compass lag fastest?

Set AK8963 to CONT2 (100 Hz). 2. Raise Pi I2C to 400 kHz. 3. Increase DLPF bandwidth and read on INT. Expect smoother, denser updates. [AK8963 Datasheet, 2013]

What’s a good fusion approach for snappy heading?

Use a complementary or Mahony/Madgwick filter with high trust in gyro for short-term motion and magnetometer for drift correction at 100 Hz. Keep magnetometer calibration current. [MPU-9250 Product Spec, 2014]

Any “gotchas” when switching AK8963 modes?

Yes. Always go to power-down first, then wait at least 100 µs before selecting Single or Continuous modes. Skipping this can give stale or invalid data. [AK8963 Datasheet, 2013]

What is the expected sample density after tuning?

With AK8963 at 100 Hz and prompt reads, you can log ~100 heading updates per second. Gyro/accel remain at up to 1 kHz for fusion stability. [AK8963 Datasheet, 2013]

How do I confirm my settings are working?

Log timestamps for each magnetometer read and compute inter-sample intervals. You should see ~10 ms spacing at 100 Hz, with minimal jitter if using INT. [AK8963 Datasheet, 2013]