RTIMULib2 is the simplest way to connect a 9-dof, 10-dof or 11-dof IMU to an embedded Linux system and obtain RTQF or Kalman-filtered quaternion or Euler angle pose data. Basically, two simple function calls (IMUInit() and IMURead()) are pretty much all that's needed to integrate RTIMULib2.
RTIMULib2 is the development version of the original RTIMULib library. So far, the main change is the addition of the runtime magnetometer calibration functionality.
*** Magnetometer calibration is critical for good performance and, with some IMU chips, meaningful fusion results will not be obtained at all unless the magnetometers have been calibrated ***
The Linux directory contains the main demo apps for embedded Linux systems:
- RTIMULibDrive is a simple app that shows to to use the RTIMULib library in a basic way.
- RTIMULibDrive10 adds support for pressure/temperature sensors.
- RTIMULibDrive11 adds support for pressure/temperature/humidity sensors.
- RTIMULibCal is a command line calibration tool for the magnetometers and accelerometers.
- RTIMULibvrpn shows how to use RTIMULib with vrpn.
- RTIMULibDemo is a simple GUI app that displays the fused IMU data in real-time.
- RTIMULibDemoGL adds OpenGL visualization to RTIMULibDemo.
RTIMULib is a C++ library but there are also Python bindings in Linux/python. It's easy to build and install the Python RTIMULib library using the provided setup.py after which any Python script will have access to RTIMULib functionality. See Linux/python/README.md (https://github.com/richards-tech/RTIMULib2/blob/master/Linux/python/README.md) for more details. Two demo scripts show how to use the Python interface, and there is a new python script to generate fuse pose data from logged IMU data. This new post-processing script shows how to read in a csv file that logs the timestamp in milliseconds and the raw accel, gyro, and magnetometer data from the IMU. It loads it and the calibration settings ini file and uses that to generate out the fused pose data and saves that back to another csv file for analysis. A sample data file is included.
Check out www.richards-tech.com for more details, updates and news (note: this link resolves back to the upstream github org which appears dead).
RTIMULib currently supports the following IMUs:
- InvenSense MPU-9150 single chip IMU.
- InvenSense MPU-6050 plus HMC5883 magnetometer on MPU-6050's aux bus (handled by the MPU-9150 driver).
- InvenSense MPU-6050 gyros + acclerometers. Treated as MPU-9150 without magnetometers.
- InvenSense MPU-9250 single chip IMU (I2C and SPI).
- InvenSense MPU-9255 single chip IMU (I2C and SPI).
- STM LSM9DS0 single chip IMU.
- STM LSM9DS1 single chip IMU.
- L3GD20H + LSM303D (optionally with the LPS25H) as used on the Pololu AltIMU-10 v4.
- STM LSM6DS33 + LIS3MDL (optionally with the LPS25H) as used on the Pololu MinIMU-9 v5 and AltIMU-10 v5.
- L3GD20 + LSM303DLHC as used on the Adafruit 9-dof (older version with GD20 gyro) IMU.
- L3GD20H + LSM303DLHC (optionally with BMP180) as used on the new Adafruit 10-dof IMU.
- Bosch BMX055 (although magnetometer support is experimental currently).
- Bosch BNO055 IMU with onchip fusion. Note: will not work reliably with RaspberryPi/Pi2 due to clock-stretching issues.
The LSM9DS1 implementation was generously supplied by XECDesign.
Pressure/temperature sensing is supported for the following pressure sensors:
- BMP180
- LPS25H
- MS5611
- MS5637
Humidity/temperature sensing is supported for the following humidity sensors:
- HTS221
- HTU21D
The humidity infrastructure and HTS221 support was generously supplied by XECDesign. It follows the model used by the pressure infrastructure - see RTIMULibDrive11 for an example of how to use this.
Note that currently only pressure and humidity sensors connected via I2C are supported. Also, an MS5637 sensor will be auto-detected as an MS5611. To get the correct processing for the MS5637, edit the RTIMULib.ini file and set PressureType=5.
By default, RTIMULib will try to autodiscover IMUs, pressure and humidity sensors on I2C and SPI busses (only IMUs on the SPI bus). This will use I2C bus 1 and SPI bus 0 although this can be changed by hand editing the .ini settings file (usually called RTIMULib.ini) loaded/saved in the current working directory by any of the RTIMULib apps. RTIMULib.ini is self-documenting making it easy to edit. Alternatively, RTIMULibDemo and RTIMULibDemoGL provide a GUI interface for changing some of the major settings in the .ini file.
RTIMULib also supports multiple sensor integration fusion filters such as RTQF and Kalman filters.
Two types of platforms are supported:
-
Embedded Linux. RTIMULib is supported for the BeagleBone (debian), Raspberry Pi (Raspbian), and Intel Edison. Demo apps for these can be found in the Linux directory and instructions for building and running can be found there. Its prerequisites are very simple - just I2C support on the target system along with the standard build-essential (included in the Raspberry Pi Raspbian distribution by default).
-
Desktop (Ubuntu/Windows/Mac). There are two apps (RTHostIMU and RTHostIMUGL) that allow the sensor fusion to be separated from the sensor interfacing and data collection. An Arduino (running the RTArduLinkIMU sketch from the RTIMULib-Arduino repo) fitted with an IMU chip collects the sensor data and sends it to the desktop. RTHostIMU and RTHostIMUGL (this one has an OpenGL visualization of the data) communicate with the Arduino via a USB connection.
The MPU-9250 and SPI driver code is based on code generously supplied by [email protected] (www.clickdrive.io). I am sure that any bugs that may exist are due to my integration efforts and not the quality of the supplied code!
RTIMULib is licensed under the MIT license.
This is the actual RTIMULib library source. Custom apps only need to include this library.
This directory contains the embedded Linux demo apps (for BeagleBone/Raspberry Pi and Intel Edison) and also the Python interface to RTIMULib. See Linux/README.md (https://github.com/VCTLabs/RTIMULib2/blob/master/Linux/README.md) for build and setup details.
RTHost contains the two apps, RTHost and RTHostGL, that can be used by desktops that don't have direct connection to an IMU (as they don't have I2C or SPI interfaces). An Arduino running RTArduLinkIMU from the RTIMULib-Arduino repo provides the hardware interface and a USB cable provides the connection between the desktop and the Arduino.
This contains Octave code used by the ellipsiod fit data generation in RTIMULibCal, RTIMULibDemo, RTIMULibDemoGL, RTHostIMU and RTHostIMUGL. It's important that a copy of this directory is at the same level, or the one above, the app's working directory or ellipsoid fit data generation will fail.
For many IMUs, fused data may be completely unusable unless the magnetometers have been calibrated.
RTIMULib2 has two mechanisms that can be used to calibrate the magnetometers:
-
Manual calibration. This is where a tool such as RTIMULibCal has been used to add magnetometer calibration data to the RTIMULib.ini file. Once this is done, there should be no need to repeat calibration unless the magnetic environment changes. Check the Calibration.pdf document for more details regarding manual calibration.
-
Runtime calibration. This mechanism is used if there is no manual calibration data. The magnetometers will remain uncalibrated until a sufficient range of readings has been obtained in each of the axes. The code will continue to monitor magnetometer readings for new maxima and minima and update the calibration data as required. This data is not saved so the procedure will start from scratch if the code is restarted.
Also, if using a non-standard axis rotation, magnetometer calibration (and accelerometer calibration if that has been performed) MUST be run AFTER changing the axis rotation.
##Note: This is a fork of richards-tech's library - this version adds support for an IMU (no-name brand) containing an HMC5883L compass, an ADXL345 accelerometer, and an L3G4200D gyro. This support is experimental and currently incomplete. Some stuff is hard-coded that should be configurable, but it generally works reliably and gets accurate data from the sensors.