Tag Archives: raspberry pi

Using python with a GPS receiver on a Raspberry Pi

Here are three examples of how to  use python to get GPS data from a GPS receiver attached to a Raspberry Pi.

  1. Using GPSD client libraries
  2. Manually parsing NMEA sentences
  3. Using  pynmea2 to parse NMEA sentences

GPSD client libraries

The gpsd client libraries are based on JSON. The JSON objects have a “class” attribute (E.g. TPV,  SKY, DEVICE.etc…)  which can be used to filter on different information.

This guide shows how to get gpsd up an running on a Raspberry Pi.

The example python  script below filters on the TPV class, which is the Time Position Velocity report and then prints out the relevant information.

#! /usr/bin/python

from gps import *
import time
   
gpsd = gps(mode=WATCH_ENABLE|WATCH_NEWSTYLE) 
print 'latitude\tlongitude\ttime utc\t\t\taltitude\tepv\tept\tspeed\tclimb' # '\t' = TAB to try and output the data in columns.
  
try:


	while True:
		report = gpsd.next() #
		if report['class'] == 'TPV':
			
			print  getattr(report,'lat',0.0),"\t",
			print  getattr(report,'lon',0.0),"\t",
			print getattr(report,'time',''),"\t",
			print  getattr(report,'alt','nan'),"\t\t",
			print  getattr(report,'epv','nan'),"\t",
			print  getattr(report,'ept','nan'),"\t",
			print  getattr(report,'speed','nan'),"\t",
			print getattr(report,'climb','nan'),"\t"

		time.sleep(1) 

except (KeyboardInterrupt, SystemExit): #when you press ctrl+c
	print "Done.\nExiting."

 

 

This python script filters on the SKY class and prints out satellite information.




#! /usr/bin/python

from gps import *
import time
import os
   
gpsd = gps(mode=WATCH_ENABLE|WATCH_NEWSTYLE) 
  
try:
	while True:
		
		report = gpsd.next() #
		if report['class'] == 'SKY':
			os.system('clear')
			print ' Satellites (total of', len(gpsd.satellites) , ' in view)'
			for i in gpsd.satellites:
				print 't', i

		
			print '\n\n'
			print 'PRN = PRN ID of the satellite. 1-63 are GNSS satellites, 64-96 are GLONASS satellites, 100-164 are SBAS satellites'
			print 'E = Elevation in degrees'
			print 'As = Azimuth, degrees from true north'
			print 'ss = Signal stength in dB'
			print 'used = Used in current solution?'

		time.sleep(1) 


except (KeyboardInterrupt, SystemExit): #when you press ctrl+c
	print "Done.\nExiting."


BerryGPS Raspberry Pi GPS

Manually parsing NMEA sentences

The python script below shows how to access GPS data by connecting directly to the serial interface.
It filters on $GPRMC NMEA sentences and then splits the well know attributes into different variables.



import serial

port = "/dev/serial0"

def parseGPS(data):
#    print "raw:", data #prints raw data
    if data[0:6] == "$GPRMC":
        sdata = data.split(",")
        if sdata[2] == 'V':
            print "no satellite data available"
            return
        print "---Parsing GPRMC---",
        time = sdata[1][0:2] + ":" + sdata[1][2:4] + ":" + sdata[1][4:6]
        lat = decode(sdata[3]) #latitude
        dirLat = sdata[4]      #latitude direction N/S
        lon = decode(sdata[5]) #longitute
        dirLon = sdata[6]      #longitude direction E/W
        speed = sdata[7]       #Speed in knots
        trCourse = sdata[8]    #True course
        date = sdata[9][0:2] + "/" + sdata[9][2:4] + "/" + sdata[9][4:6]#date

        print "time : %s, latitude : %s(%s), longitude : %s(%s), speed : %s, True Course : %s, Date : %s" %  (time,lat,dirLat,lon,dirLon,speed,trCourse,date)

def decode(coord):
    #Converts DDDMM.MMMMM > DD deg MM.MMMMM min
    x = coord.split(".")
    head = x[0]
    tail = x[1]
    deg = head[0:-2]
    min = head[-2:]
    return deg + " deg " + min + "." + tail + " min"


print "Receiving GPS data"
ser = serial.Serial(port, baudrate = 9600, timeout = 0.5)
while True:
   data = ser.readline()
   parseGPS(data)


 

Using  pynmea2 to parse NMEA sentences

The python script below shows how to access GPS data by connecting directly to the serial interface.
It filters on $GPGGA NMEA sentences and then uses pynmea2 to parse the data.

Pynmea2 can be installed with;

pi@raspberrypi ~ $ pip install pynmea2

import serial
import pynmea2

port = "/dev/serial0"

def parseGPS(str):
    if str.find('GGA') > 0:
        msg = pynmea2.parse(str)
        print "Timestamp: %s -- Lat: %s %s -- Lon: %s %s -- Altitude: %s %s -- Satellites: %s" % (msg.timestamp,msg.lat,msg.lat_dir,msg.lon,msg.lon_dir,msg.altitude,msg.altitude_units,msg.num_sats)


serialPort = serial.Serial(port, baudrate = 9600, timeout = 0.5)
while True:
    str = serialPort.readline()
    parseGPS(str)

Raspberry Pi Embedded Cap With GPS & 10DOF

In this post we will show you how to geotag and capture the “attitude”  of photos taken with the Raspberry Pi camera and record these values within the photo itself using EXIF metadata

We used a modified (hacked?) cap to take the images in this post. The cap took photos, geo-tagged and recorded attitude as we walked around Sydney Harbour.

Components used were;

  • Raspberry Pi Zero W
  • BerryGPS-IMU
  • Raspberry Camera V2
  • A cap

The BerryGPS-IMU was used to capture the GPS coordinates as well as “attitude”.   No external antenna was needed as the BerryGPS-IMU includes an internal antenna.

The “attitude” would include values such as pitch, roll, direction. Some of this data you can see annotate in the image below.


raspberry pi camera gps

Other programs can use some of this data to plot the image on a map and even show the direction of the camera at the time the image was taken.  A good example of this is seen in  GeoSetter

Camera attitude

 

The Cap

The cap has the BerryGPS-IMU sitting on top of the visor, with the Raspberry Pi sitting under the viso.  Some holes where made in the visor to allow connectivity between the BerryGPS-IMU and Raspberry Pi.
We also created a basic camera mount out of 3mm laser cut acrylic. M2.5 Nylon screws were used to hold everything in place.
Raspberry Pi GPS

 

Continue reading Raspberry Pi Embedded Cap With GPS & 10DOF

Navigating with Navit on the Raspberry Pi

 

Navit is an open source navigation system with GPS tracking.
It works great with a Raspberry Pi,  a GPS module and a small TFT with touch, jut like the official Raspberry Pi Display or PiScreen.

 

In this guide, we will be using;

Setting up the GPS

Navit can be installed without a GPS connected to your Raspberry Pi, but you will not be able to use the real-time turn by turn navigation. You will however be able to browse maps. If you are not going to use a GPS, you can skip to the next step.

As we are using the BerryGPS-IMU, we will be following the guide in the link below.  As most GPS modules use serial to communication, this guide can be followed for other GPS modules.

BerryGPS Setup Guide for the Raspberry Pi

 

The images below shows how we have connected the BerryGPS-IMU to the Raspberry Pi 3 whilst it is in the SmartPi Touch case.


Raspberry Pi Navit GPS

If you plan on testing this out in your car,  you need to be mindfully of where you place your BerryGPS. In my setup and I have placed it in the air vent as shown below, and BerryGPS gets a good strong signal.

Raspberry Pi GPS

If you are using an external antenna, then there is no need to worry about where your BerryGPS is placed.

Continue reading Navigating with Navit on the Raspberry Pi

BerryIMU running on Raspberry Pi running Windows IoT

BerryIMU also works great with Windows IoT Core on the Raspberry Pi.

Our Git repository contains the source files needed to get the BerryIMU up and running on Windows IoT.

The code will print out the following values to the screen;

  • Raw values from the gyroscope, accelerometer and magnetometer.
  • Accelerometer calculated angles.
  • Gyro tracked angles.
  • Fused X and Y angles.
  • Heading.
  • Tilt compensated heading.

Connecting BerryIMU to a Raspberry Pi

BrryIMU can connect via the jumper cables to the Raspberry Pi as shown below;
Raspberry Pi BerryIMU

IMU Raspberry Pi Accelerometer gyro

Or BerryIMU can sit right on top of the GPIO pins on a Raspberry Pi. The first 6 GPIOs are used as shown below.

IMU Raspberry Pi Accelerometer gyro

Get the Code

Download the BerryIMU code for Windows IoT from our GIT repository. The files you need are under the WindowsIoT-BerryIMU folder.

You will need to download the entire git repository as GIT doesn’t allow downloading individual folders.

Once downloaded, double-click  the file WindowsIoT-BerryIMU.sln to open up the project in Visual Studio.

 

About the code

The project code outputs all of the needed values to the screen and a complementary filter is used to fuse the accelerometer and gyroscope angles.

We have a number of guides already documented on how to get the BerryIMU working with the Raspberry Pi.
http://ozzmaker.com/berryimu/
These are based on Raspbian, however the principals and math are the same for Windows Iot.

The final values which should be used are the fused X &Y angles and the tilt compensated heading.

The sensor on the BerryIMU is the LSM9DS0 and all the I2C registers for this sensor can be found in LSM9DS0.cs

The main code can be found in MainPage.xaml.cs

 

BerryIMU Raspberry Pi Gyroscope Accelerometer

Complementary Filter

A complementary filter is used to fuse the angles. Is summary, the complementary filter trusts the gyroscope for short periods and trusts the accelerometer for longer periods;

CFangleX = AA * (CFangleX + (rate_gyr_x * DT / 1000)) + (1.0f - AA) * AccXangle;
CFangleY = AA * (CFangleY + (rate_gyr_y * DT / 1000)) + (1.0f - AA) * AccYangle;

Changing how much trust is given for each of the sensors can be changed by modify the complementary filter constant at the start of the code.

const float AA = 0.03f;     // Complementary filter constant

Loop Speed

The loop speed is important as we need to know how much time has past to calculate the rotational degrees per second on the gyroscope.
A time delta is set at the start of the code.

const int DT = 100;         //DT is the loop delta in milliseconds.

This is then used to specify a new timer method.

periodicTimer = new Timer(this.TimerCallback, null, 0,DT);

Here you can see where DT is used to keep track of the gyroscope angle. You can also see it in the above calculation for the complementary filter.

//Calculate the angles from the gyro
gyroXangle += rate_gyr_x * DT / 1000;
gyroYangle += rate_gyr_y * DT / 1000;
gyroZangle += rate_gyr_z * DT / 1000;

BerryIMU orientation

The calculations in the code are based on how the BerryIMU is orientated. If BerryIMU is upside down, then some of the angles need to be reversed. It is upside down when the skull logo is facing up(or to the sky).
If it is upside down, set the below value to true. Otherwise, set it to false.

bool IMU_upside_down = true;

 

 

 

.

GPS Data logger using a BerryGPS

This post explains how to log GPS data from a BerryGPS or a BerryGPS-IMU and then how to plot this data onto Google Maps and many other maps E.g. OpenStreet, WorldStreet, National Maps, etc..

Raspberry Pi GPS

1. Setup GPS

Follow the instructions on this page to setup your Raspberry Pi for a BerryGPS. Ensure GPSD is set to automatically start and confirm that you can see the NMEA sentences when using gpsipe;

pi@raspberrypi ~ $ gpspipe -r

 

2.  Automatically Capture Data on Boot.

We will be using gpspipe to capture the NMEA sentence from the BerryGPS and storing these into a file. The command to use is;

pi@raspberrypi ~ $ gpspipe -r -d -l -o /home/pi/`date +”%Y%m%d-%H-%M-%S”`.nmea

-r = Output raw NMEA sentences.
-d = Causes gpspipe to run as a daemon.
-l = Causes gpspipe to sleep for ten seconds before attempting to connect to gpsd.
-o = Output to file.

The date the file is created is also added to the name.

Now we need to force the above command to run at boot. This can be done by editing the rc.local file.

pi@raspberrypi ~ $ sudo nano /etc/rc.local

 

Just before the last line, which will be ‘exit 0’, paste in the below line;

gpspipe -r -d -l -o /home/pi/`date +"%Y%m%d-%H-%M-%S"`.nmea

Reboot and confirm that you can see a .nmea file in the home directory.

Continue reading GPS Data logger using a BerryGPS

Boot Raspberry Pi from USB – in Beta

The Raspberry Pi 3 can now be booted from a USB drive or from over the Network.  It is still in beta and somewhat complicated to setup.

Raspberry Pi Network Boot

 

However, once the Raspberry Foundation has ironed out some of the bugs and have made it easier to configure, I think these two features will be used more frequently, specifically booting from USB.

 

Raspberry Pi USB

 

 

Info here;
Raspberry Pi boot modes

And below;

Pi 3 booting part I: USB mass storage boot beta

 

Testing points on a Raspberry Pi

Below is a list of test points which can be found on Raspberry Pi 2, 3 and some are also on b+.

With the use of a multimeter, these test points can help with troubleshooting hardware issues.

I have yet to find any formal documentation about these test points. However, I do know they exist.

PP3 GND
PP4 GND
PP5 GND
PP6 GND
PP7 5V after polyfuse
PP8 3V3
PP9 1V8
PP10 Goes from 3V3 to 2V on brownout
PP11 DAC_2V5 (for composite video DAC)
PP12 AUD_2V5 (for PWM audio drivers)
PP13 Goes from 3V3 to 2V on ACT activity
PP14 SD_CLK
PP15 SD_CMD
PP16 SD_DAT0
PP17 SD_DAT1
PP18 SD_DAT2
PP19 SD_DAT13
PP20 H5V
PP21 RUN signal (reset)
PP22 Goes from 3V3 to 2V on activity of green (link) ethernet jack LED
PP23 Goes from 3V3 to 2V on activity of yellow (speed) ethernet jack LED
PP24 COMPVID
PP25 AUDIO_L
PP26 AUDI_R
PP27 VBUS (USB 5V power)
PP28 ETH_CLK (25.000 MHz)
PP29 VC_TMS
PP30 VC_TRST_N
PP31 VC_CLK
PP32 VC_TDI
PP33 VC_TDO
PP34 GND
PP35 GPIO6 of LAN9514
PP36 GPIO7 of LAN9514
PP37 CAM_GPIO0
PP38 CAM_GPIO1
PP39 SCL0
PP40 SDA0

 

Below is an example of how you can use PP9 to confirm that  the regulator is supplying 1.8v correctly.

Ground can be sourced from the SD card slot;Pi3TestPointsProbs-1000

And the exact location of PP9;

Raspberry Pi test point 9

 

Adafruit has some great information covering the Raspberry Pi power circuitry. link

 

 

How to Check the Software and Hardware Version of a Raspberry Pi

There are a number of commands which can be used to check the hardware and software versions on a Raspberry Pi.

Version of Debian;

cat /etc/debian_version can be used to see what version of Debian you are running.

pi@raspberrypi ~ $ cat /etc/debian_version
7.8

2015-05-05-raspbian-wheezy

pi@raspberrypi ~ $ cat /etc/debian_version
8.0

2016-02-03-raspbian-jessie

OS Release Notes;

cat /etc/os-release can be used to see OS release notes

pi@raspberrypi ~ $ cat /etc/os-release
PRETTY_NAME=”Raspbian GNU/Linux 7 (wheezy)”
NAME=”Raspbian GNU/Linux”
VERSION_ID=”7″
VERSION=”7 (wheezy)”
ID=raspbian
ID_LIKE=debian
ANSI_COLOR=”1;31″
HOME_URL=”http://www.raspbian.org/”
SUPPORT_URL=”http://www.raspbian.org/RaspbianForums”
BUG_REPORT_URL=”http://www.raspbian.org/RaspbianBugs”

2015-05-05-raspbian-wheezy

pi@raspberrypi ~ $ cat /etc/os-release
PRETTY_NAME=”Raspbian GNU/Linux 8 (jessie)”
NAME=”Raspbian GNU/Linux”
VERSION_ID=”8″
VERSION=”8 (jessie)”
ID=raspbian
ID_LIKE=debian
HOME_URL=”http://www.raspbian.org/”
SUPPORT_URL=”http://www.raspbian.org/RaspbianForums”
BUG_REPORT_URL=”http://www.raspbian.org/RaspbianBugs”

2016-02-03-raspbian-jessie

Kernel Version;

uname -a can be used to see what kernel version is running

pi@raspberrypi ~ $ uname -a
Linux raspberrypi 3.18.11-v7+ #781 SMP PREEMPT Tue Apr 21 18:07:59 BST 2015 armv7l GNU/Linux

2015-05-05-raspbian-wheezy

pi@raspberrypi ~ $ uname -a
Linux raspberrypi 4.1.19-v7+ #858 SMP Tue Mar 15 15:56:00 GMT 2016 armv7l GNU/Linux

2016-02-03-raspbian-jessie

 

BerryIMU Raspberry Pi Gyroscope Accelerometer

To check the hardware version;

cat /proc/cpuinfo can be used to see what hardware you are using. Take note of the revision number in the second last line and then refer to the table below. The output below is from a Pi 2

pi@raspberrypi ~ $ cat /proc/cpuinfo
processor : 0
model name : ARMv7 Processor rev 5 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xc07
CPU revision : 5processor : 1
model name : ARMv7 Processor rev 5 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xc07
CPU revision : 5processor : 2
model name : ARMv7 Processor rev 5 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xc07
CPU revision : 5processor : 3
model name : ARMv7 Processor rev 5 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xc07
CPU revision : 5Hardware : BCM2709
Revision : a21041
Serial : 00000000c15e9432
pi@raspberrypi ~ $

 

 

Model and Pi Revision 256MB Hardware Revision Code from cpuinfo
Model B Revision 1.0 256MB 0002
Model B Revision 1.0 + ECN0001 (no fuses, D14 removed) 256MB 0003
Model B Revision 2.0
Mounting holes
256MB 0004
0005
0006
Model A
Mounting holes
256MB 0007
0008
0009
Model B Revision 2.0
Mounting holes
512MB 000d
000e
000f
Model B+ 512MB 0010
Compute Module 512MB 0011
Model A+ 256MB 0012
Pi 2 Model B 1GB a01041 (Sony, UK)
a21041 (Embest, China)
PiZero 512MB 900092(no camera connector)
900093(camera connector)
Pi 3 Model B 1GB a02082 (Sony, UK)
a22082 (Embest, China)
PiZero W 512MB 9000c1

Raspberry Pi Digital Spirit Level

 

In this post we show how to create a Digital Spirit Level using a Raspberry Pi and python.

The code moves that bubbles on the display in relation to the angle read from the IMU.
Parts used in this project;

Any IMU or TFT can be used, however the code would need to be updated to accommodate the different devices. It is best to use a 480×320 TFT as the images are scaled to fit this resolution.

This guide assumes that some basic understanding of an IMU(Accelerometer and Gyroscope)  is already known. And you have one already working with your Raspberry Pi.

If you don’t, we do have some guides which covers this.

BerryIMU Raspberry Pi Gyroscope Accelerometer

 

We have used our existing python code to read the values from the IMU, however we have removed the code related to the magnetometer as it isn’t needed for this project.

Git repository here
The code can be pulled down to your Raspberry Pi with;

pi@raspberrypi ~ $ git clone http://github.com/ozzmaker/BerryIMU.git

 

Placement of IMU

The IMU can be attached anywhere, however it is best to place it in the same orientation as shown below. If you do change the orientation, you will need to update the code accordingly.

spiritLevel1-600

Continue reading Raspberry Pi Digital Spirit Level

BerryIMU Python Code Update – Kalman Filter and More

We have updated to the python code in our git repo.

It now includes;

  • The elusive Kalman filter.
  • Math needed when the IMU is upside down
  • Automatically calculate loop period.
  • A lot more comments.

What is a Kalman filter?  In a nutshell;
A Kalman filter is, it is an algorithm which uses a series of measurements observed over time, in this context an accelerometer and a gyroscope. These measurements will contain noise that will contribute to the error of the measurement. The Kalman filter will then try to estimate the state of the system, based on the current and previous states, that tend to be more precise that than the measurements alone.

A Kalman filter is more precise than a Complementary filter. This can be seen in the image below, which is the output of a complementary filter (CFangleX) and a Kalman filter (kalmanX) from the X axis plotted in a graph.

The red line (KalmanX) is better at filtering out noisep;

Python Kalman filter Raspberry Pi

 

The code can be found here in our Git repository here
And  can be pulled down to your Raspberry Pi with;

pi@raspberrypi ~ $ git clone https://github.com/mwilliams03/BerryIMU.git

BerryIMU Raspberry Pi Gyroscope Accelerometer

A summary of the code;


def kalmanFilterY ( accAngle, gyroRate, DT):
        y=0.0
        S=0.0

        global KFangleY
        global Q_angle
        global Q_gyro
        global y_bias
        global XP_00
        global XP_01
        global XP_10
        global XP_11
        global YP_00
        global YP_01
        global YP_10
        global YP_11

        KFangleY = KFangleY + DT * (gyroRate - y_bias)

        YP_00 = YP_00 + ( - DT * (YP_10 + YP_01) + Q_angle * DT )
        YP_01 = YP_01 + ( - DT * YP_11 )
        YP_10 = YP_10 + ( - DT * YP_11 )
        YP_11 = YP_11 + ( + Q_gyro * DT )

        y = accAngle - KFangleY
        S = YP_00 + R_angle
        K_0 = YP_00 / S
        K_1 = YP_10 / S

        KFangleY = KFangleY + ( K_0 * y )
        y_bias = y_bias + ( K_1 * y )

        YP_00 = YP_00 - ( K_0 * YP_00 )
        YP_01 = YP_01 - ( K_0 * YP_01 )
        YP_10 = YP_10 - ( K_1 * YP_00 )
        YP_11 = YP_11 - ( K_1 * YP_01 )

        return KFangleY