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Hardware

A solution for outdoor localization and basic navigation is an Arduino board, such as the Adafruit Feather nRF52840 Sense with a GPS shield, such as the Adafruit Ultimate GPS FeatherWing. The Sense provides a micro controller, Bluetooth, a 6DoF IMU, and a magnetometer (a LIS3MDL). The FeatherWing provides a GPS.

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Assembly

Solder the FeatherWing to the Sense. Add support for the nRF52 to the Arduino IDE. Then, add the board - it’s the Adafruit nRF52840 Sense. The Arduino IDE will probably need to also install the nRF core library (adafruit:nrf52@1.6.1). You should now be able to connect to the board.

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Software

Add the following libraries to the Arduino IDE:
  • Adafruit GPS library
  • Adafruit LIS3MDL
  • Adafruit_LSM6DS
Run the sketch provided in /system_hw_test/gps_mag. It yields all the data for tilt compensated magnetic heading, a full AHRS solution (yaw, tilt, roll) and a GPS location, as well as altitude and velocity. WARNING The direction and AHRS data will be entirely incorrect unless you calibrate your magnetometer, gyro, and accelerometer.

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Calibration

In the /system_hw_test/gps_mag.ino, see: 
/* For calibration, set CALIBRATION to true, run the script, close
the Arduino IDE, close the serial monitor, and open MotionCal.

MotionCal can be downloaded here:

https://github.com/PaulStoffregen/MotionCal

If you do not see your Arduino in MotionCal's serial port 
drop down menu, follow these instructions to compile MotionCal 
for your OS and platform

https://github.com/PaulStoffregen/MotionCal/issues/11#issuecomment-2412937251
*/

bool CALIBRATION = false;
Once you have established your 15 (3+9+3) calibration coefficients (see https://github.com/PaulStoffregen/MotionCal), determine the device string of your sensor (provided in the initial serial debug output), and add your calibration coefficients following the example given in gap_mag.ino: 
if (strcmp(DeviceID, "38e4bfd6") == 0) {
	float mh[] = {24.420, -16.87, -3.880}; // in uTesla
	float ms[] = { 0.959,  0.050, -0.038,  \
	               0.050,  1.078,  0.002,  \
	              -0.038,  0.002,  0.970}; 
	float gc[] = { 0.000,  0.020,  0.010}; // in Radians/s
	memcpy(mag_hardiron,  mh, sizeof(mag_hardiron));
	memcpy(mag_softiron,  ms, sizeof(mag_softiron));
	memcpy(gyro_zerorate, gc, sizeof(gyro_zerorate));
} else {
	Serial.println("CAUTION: Magnetometer not calibrated - code will yield garbage - please calibrate your Magnetometer and IMU");
}
WARNING If you use different sensor hardware (and not the nRF52840 Sense with the FeatherWing), you will need to adapt all data flowing from your gyros, accelerators, and magnetometer to (1) have the correct units, (2) follow the correct sign conventions, and (3) accommodate your pcb/sensor geometry as well as how the sensors are mounted relative to your robot. This tends to be extremely tedious and time consuming, so we recommend using the Sense/FeatherWing for which this driver has been developed.

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Usage

Once your Sense/FeatherWing is streaming data on the serial line, it can be fed into OM1. See src/inputs/plugins/serial_reader for an example of how to do that.

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Finding the Arduino on Linux

When connecting to the Arduino via USB, you should see the Arduino serial port appear as /dev/ttyACM0 (sometimes the number can be different, for example /dev/ttyACM1). Run: 
sudo dmesg | grep ttyACM*
and you should see it. If you’re not sure which tty device is the Arduino board, run sudo dmesg and looks for entries with “Arduino” in it. This way you will easily spot the serial device name of your Arduino. You can read the data with: 
screen /dev/ttyACM0 115200
Note: on a typical TurtleBot4, the RPLIDAR uses /dev/ttyUSB0. The TurtleBot4 assumes that the RPLIDAR is accessible at /dev/ttyUSB0. If you change which USB port the LIDAR is plugged into, the LIDAR will fail.

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Finding the Arduino GPS Feather on Mac

Determine the serial port the sensor is using: 
ls /dev/tty.*  
# or
ls /dev/cu.*
It should be something similar to /dev/cu.usbmodem8401. Even on a mac, you can use screen to see the data: screen /dev/cu.usbmodem21201 115200

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Testing

uv run src/run.py test_gps

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RTCM / RTK Precision GPS

An RTK GPS system can provide centimeter accuracy localization. In a typical setup, an RTK-compatible GPS receiver (such as the u-blox ZED-F9P/simpleRTK2B accepts RTCM messages over XBee or the internet, and uses those data to correct (and greatly improve) its location estimate. The simpleRTK2B has two usb ports, one labeled power + GPS and the other power + XBee. The first port provides a full set of NEMA and UBX messages, including GPGGA, GNRMC, GNGLL, and UBX(NAV-PVT). The power + XBee port provides direct access to received RTK correction messages, such as 1005 and 1074. In standard usage, a rover would use the NEMA messages to track its location whilst monitoring the status, mode, and quality fields. 
<NMEA(GNGGA, time=23:56:12, lat=12.4003072367, NS=N, lon=-2.1187726617, EW=W, quality=5, numSV=12, HDOP=0.74, alt=26.189, altUnit=M, sep=-30.024, sepUnit=M, diffAge=3.0, diffStation=0)>
This GNGGA message shows a high quality RTK fix (quality=5):
  • 1 autonomous (standard accuracy) solution
  • 2 code-differential (DGNSS, SBAS) solution
  • 4 fixed RTK
  • 5 float RTK
This GNRC message shows a valid status(A) using RTK mode with fixed integers (posMode=R) 
<NMEA(GNRMC, time=23:35:31.400000, status=A, lat=12.40030754, NS=N, lon=-2.1187695133, EW=W, spd=0.036, cog=, date=2025-06-01, mv=, mvEW=, posMode=R)>
Status indicator: A: Data valid V: Data not valid Mode indicator: A: Autonomous mode D: Differential mode P: Precise E: Estimated (dead reckoning) mode M: Manual input mode S: Simulator mode R: Fixed RTK. RTK mode with fixed integers F: Float RTK. RTK mode with floating integers. N: Data not valid

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RTK Data Plotting

The best software to use is pygpsclient: 
python3 -m venv pygpsclient
source pygpsclient/bin/activate
python3 -m pip install --upgrade pygpsclient
pygpsclient