Description
EECE 5554 Robotics Sensing and Navigation
Goals
We would like to understand how to characterize and choose IMU sensors for different robotic
applications. As part of the exercise we will write a device driver for the IMU and use that to
collect data and analyze it.
Readings
We have discussed sensor calibration and sensor stochastic characterization. The Vectornav
whitepaper on the subject is included so that you can refresh your memory.
Hardware / Sensors:Vectornav VN-100 IMU. The user manual for the sensor is provided.
Hardware Setup
Imu udev rules Standard Ubuntu is not designed for roboti application and for fast sensors it
may not behave as we want it to. There may be latency in terms of its response to the sensor.
There are a set of files under /etc/udev that dictate how Ubuntu will react to certain sensors.
To make the VN-100 plug & play and to address Ubuntu USB latency issues we can do the
following
Connect your imu and type the following command in the terminal
$ dmesg | grep vn100
If you do not know what those commands mean, check out Linux tutorial.
You will see your sensor attributes including product id and vendor id. If these values differ from
the values below, change them in your 50-VN-100.rules file accordingly.
1. As Sudo create a file under /etc/udev/rules.d called 50-VN-100.rules with the following
KERNEL==”ttyUSB[0-9]*”, ACTION==”add”, ATTRS{idVendor}==”1d6b”, ATTRS{idProduct}==”0002″,
MODE=”0666″, GROUP=”dialout”
2. Ubuntu’s default latency of 16ms cause issues with high rate sensors, this can be solved
by adding the following UDEV rules as 49-USB-LATENCY.rules
ACTION==”add”, SUBSYSTEM==”usb-serial”, DRIVER==”ftdi_sio”, ATTR{latency_timer}=”1″
3. Once the rules have been added, to get udev to recognize the rule, run the following
command:
sudo udevadm control –reload-rules && sudo service udev restart && sudo udevadm trigger
Finally unplug and replug the VN-100 to have it work with the new rules.
You can verify the applied settings with below command, should report 1 on success.
EECE 5554 Robotics Sensing and Navigation Spring 2022
$ cat /sys/bus/usb-serial/devices/
Write a device driver for IMU (individual) and do stationary noise analysis
Open serial port with 115200 baudrate. Configure your IMU to output data at 200Hz for this lab.
Parse $vnymr string, to get accel, gyro, orientation (roll, pitch, yaw) and magnetometer data.
Refer to sensor user_manual.
Use the standard ROS sensor_msgs/IMU to publish the above data. Convert the above Yaw,
Pitch, Roll data into quaternions and publish it as orientation in the same imu msg. Use ROS
sensor_msgs/MagneticField to publish magnetometer data.
Begin collecting a time series data (rosbag) for the 3 accelerometers, 3 angular rate gyros, 3-
axis magnetometers. Collect at least 10-15 min of data with the instrument stationary and as far
away as possible from your computer.
Plot each time series in your report and figure out the noise characteristics of each of the values
(mean and standard deviation) reported by the IMU.
Allan Variance Data collection and Analysis
For this part we only need to collect one set of data for each team – collect roughly 5 hours
worth of stationary IMU data at a location that is not subject to vibrations (passing trains,
building sway etc).
The following MathWorks webpage provides code that you can use to analyze your data for Allan
variance. We do not need any further analysis for Allan Variance other than that illustrated in
this code.
https://www.mathworks.com/help/nav/ug/inertial-sensor-noise-analysis-using-allanvariance.html
You need to analyze this data using the Matlab functions and should be able to answer the
following questions.
1. What kind of errors/sources of noise are present?
2. How do we model them? Where do we measure them? Can you relate your
measurements to the datasheet for the VN100?
How to Submit Lab_3
1. In your class repo ‘EECE5554_RoboticsSensing’, create a directory called LAB3
2. Copy the ros driver package used for this assignment under LAB3.
3. Inside LAB3. create a sub-directory called ‘analysis’
4. Place your report in pdf format also in the analysis directory. Your report includes
analysis for both the stationary data you collected individually, as well as the Matlab
analysis on the data on Allan variance collected as a team.
5. Place any matlab / python code you used to generate and plot your noise parameters in
this directory as well.
EECE 5554 Robotics Sensing and Navigation Spring 2022
Your repo structure should look similar to
‘
‘
‘
6. Push your local commits to (remote) gitlab server. You can verify this by visiting
gitlab.com and making sure you can see the commit there.
Everyone in the team needs to write their own device driver for the IMU. Make sure
everyone gets a fair amount of sensor time to test their imu driver, and to analyze sensor noise.
You can use the emulator to write your driver even when you do not physically have access to
the device. We have provided a sample IMU data file for your use.
Analysis on the collected dataset, should be done individually.
