Optical-Inertial Sensor Fusion

Optical position tracking and inertial orientation tracking are well established measurement methods. Each of these methods has its specific advantages and disadvantages. In this post we show an opto-inertial sensor fusion algorithm that joins the capabilities of both to create a capable system for position and orientation tracking.

How It Works

The reliability of position and orientation data provided by an optical tracking system (outside-in or inside-out) can for some applications be compromised by occlusions and slow system reaction times. In such cases it makes sense to combine optical tracking data with information from an inertial measurement unit located on the device. Our optical-intertial sensor fusion algorithm implements this functionality for integration with an existing tracking system or for the development of a novel system for a specific application case.

The graphs below show two examples of how the signal from an optical positioning system can be improved using inertial measurements. Slow camera framerates or occasional drop-outs are compensated by information from the integrated inertial measurement unit, improving the overall tracking performance.

Combination of Several Optical Trackers

For a demonstration, we combined three NEXONAR IR trackers and an LPMS-B2 IMU, mounted together as a hand controller. The system allows position and orientation tracking of the controller with high reliability and accuracy. It combines the strong aspects of outside-in IR tracking with inertial tracking, improving the system’s reaction time and robustness against occlusions.

Optical-Inertial Tracking in VR

The tracking of virtual reality (VR) headsets is one important area of application for this method. To keep the user immersed in a virtual environment, high quality head tracking is essential. Using opto-inertial tracking technology, outside-in tracking as well as inside-out camera-only tracking can be significantly improved.

Robot Operating System and LP-Research IMUs? Simple!

Robot Operating System (ROS) is a tool commonly used in the robotics community to pass data between various subsystems of a robot setup. We at LP-Research are also using it in various projects, and it is actually very familiar to our founders from the time of their PhDs. Inertial Measurement Units are not only a standard tool in robotics, the modern MEMS devices that we are using in our LPMS product line are actually the result of robotics research. So it seemed kind of odd that an important application case for our IMUs was not covered by our LpSensor software: namely, we didn’t provide a ROS driver.  We are very happy to tell you that such a driver exists, and we are happy that we don’t have to write it ourselves: the Larics laboratory  at the University of Zagreb are avid users of both ROS and our LPMS-U2 sensors. So, naturally, they developed a ROS driver which they provide on their github site.  Recently, I had a chance to play with it, and the purpose of this blog post is to share my experiences with you, in order to get you started with ROS and LPMS sensors on your Ubuntu Linux system.

Installing the LpSensor Library

Please check our download page for the latest version of the library, at the time of this writing it is 1.3.5. I downloaded it, and then followed these steps to unpack and install it:

I also installed libbluettoth-dev, because without Bluetooth support, my LPMS-B2 would be fairly useless.

Setting up ROS and a catkin Work Space

If you don’t already have a working ROS installation, follow the ROS Installation Instructions to get started. If you already have a catkin work space you can of course skip this step, and substitute your own in what follows.  The work space is created as follows, note that you run catkin_init_workspace inside the src sub-directory of your work space.

Downloading and Compiling the ROS Driver for LPMS IMUs

We can now download the driver sources from github. It optionally makes use of and additional ROS module by the Larics laboratory which synchronizes time stamps between ROS and the IMU data stream.  Therefore, we have to clone two git repositories to obtain all prerequisites for building the driver.

That’s it, we are now ready to run catkin_make to get everything compiled and ready.  Building was as simple as running catkin_make, but you should setup the ROS environment before that.  If you haven’t, here’s how to do that:

This should go smoothly. Time for a test.

Not as Cool as LpmsControl, but Very Cool!

Now that we are set up, we can harness all of the power and flexibility of ROS. I’ll simply show you how to visualize the data using standard ROS tools without any further programming.  You will need two virtual terminals.  In the first start roscore, if you don’t have it running yet.  In the second, we start rqt_plot in order to see the data from our IMU, and the lpms_imu_node which provides it.  In the box you can see the command I use to connect to my IMU. You will have to replace the _sensor_model and _port strings with the values corresponding to your device.  Maybe it’s worth pointing out that the second parameter is called _port, because for a USB device it would correspond to its virtual serial port (typically /dev/ttyUSB0).

Once you enter these commands, you will then see the familiar startup messages of LpSensor as in the screenshot below. As you can see the driver connected to my LPMS-B2 IMU right away. If you cannot connect, maybe Bluetooth is turned off or you didn’t enter the information needed to connect to your IMU.  Once you have verified the parameters, you can store them in your launch file or adapt the source code accordingly.

Screenshot starting LPMS ROS node

Screenshot of starting the LPMS ROS node

The lpms_imu_node uses the standard IMU and magnetic field message types provided by ROS, and it publishes them on the imu topic.  That’s all we need to actually visualize the data in realtime.  Below you can see how easy that is in rqt_plot. Not as cool as LpmsControl, but still fairly cool. Can you guess how I moved my IMU?

animation of how to display LPMS sensor data in ROS

Please get in touch with us, if you have any questions, or if you found this useful for your own projects.

Update: Martin Günther from the German Research Center for Artificial Intelligence was kind enough to teach me how to pass ROS parameters on the command line.  I’ve updated the post accordingly.

New Miniature Sensor In: LPMS-ME1 Maker Edition!


The LPMS-ME1’s Maker Edition is miniature-sized with just 12 x 12 x 2.6 mm.

We proudly present you our latest development! The LPMS-ME1 is our smallest motion sensor so far, with just 12 x 12 x 2.6 mm it is tiny! Despite its size this powerful 9-axis inertial measurement unit (IMU) has several sensors integrated, for example a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer. And this miniature motion sensor certainly comes at low cost.

It is very easy to assemble and can be conveniently embedded in the system of your choice. Due to its size it is perfect for your design ideas and development projects. Just to to give you some inspiration, it can be used for human motion capture or sports performance evaluation, for various sorts of Internet of Things (IoT) devices, and can be used to control unmanned aerial vehicles. You can even fly a drone with it!

Have a look at more specifications in our data sheet. This sensor comes with our own LpmsControl utility software and a one-year warranty service.

Get the LPMS-ME1 Maker Edition for your own innovations! Find a distributor of your choice or order online at Zenshin Tech.



Activity Recognition & Context Analysis


We wanted to determine the different modes of transport a user is taking on his or her daily routine, whether he or she is walking, running, or even cycling, and whether he or she is using a train or driving in a car. Our IMU Bluetooth sensor connected to our own developed Android app was able to give us plenty of insights. We think this could ideally be integrated in all sorts of health or insurance applications, for example.

All About Activity Recognition & Context Analysis – Sensing Different Modes Of Transport

We recently conducted a test using our IMU sensors for activity recognition. We built our own machine learning algorithm to determine the different modes of transport of a user on his or her path throughout the day: Resting, walking, running, riding in a car, or riding on a train.

For this purpose, we developed an Android app for a smartphone connected it with our LPMS-B2 Bluetooth sensor. Therefore we could receive the sensor data with the accelerometer, the gyroscope, the magnetometer, and the microphone of the user’s smartphone. Important was for us to be able to determine whether the user actually does physical exercise by walking, running or even cycling. Moreover we wanted to know what modes of transport he or she uses, either by car or by train.

Possible Purposes

With our experiment we wanted to get plenty of data that could be used for a variety of consumer applications. Not only could we determine the health and mental condition of the user. We could also assess whether he or she was in a risk environment. Apart from modal transport applications our setup would be ideal to be integrated in applications for personal assistance, insurance, or health, for example.

Our Test Setup

For the test setup the user would move around in the city with her smartphone and our connected IMU sensor in her pocket. The sensor would monitor her activity throughout the day; the smartphone app would give out the analysis. When there was pedometer activity detected, then the system would determine whether the user was walking or running. When there was a magnetic interference, the system knew the user was taking a train.

When the app detected noise through the smartphone’s microphone or acceleration, the user was driving in a car.


The Results

  • We recorded more than 1,000,000 points of training and test data altogether
  • Based on our machine learning method we developed a new algorithm that we also plan to implement on our next generation sensor hardware
  • The latest adjustment of our algorithm resulted in average detection quality of 92.1%!

Planned Future Work

  • After this successful setup we will be recording more training and test data.
  • We will further optimize our newly developed algorithm.
  • By the application of deep learning methods we intend to extend the capabilities of our algorithm
Activity Recognition trial-result

New Distributor: Visit The Zenshin Tech Shop For Our Motion Sensors

Zenshin Tech screenshot

New worldwide distribution by Zenshin Technology Ltd

Getting our IMU sensors online has just become so much easier. Create your own innovations and simply order at our new worldwide distributor: Zenshin Technology Ltd is an online shop operating from Hong Kong. Have a look at our next generation IMUs and explore the optimized features of for example, the LPMS-CURS2 (9-axis motion sensor with USB, CAN bus and UART connectivity) or the LPMSs-CANAL2 (9-axis IMU with CAN connectivity and waterproof housing).

In addition to our distributors in every region, you can get LP-RESEARCH’s IMU sensors now from Zenshin Tech. Order comfortably with worldwide express shipping from here: https://zenshin-tech.com

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