About Tobias Schlüter

Before I came to LP-Research, I used to do particle physics research. In my former career I studied hadrons (i.e. particles composed from quarks) at various particle accelerator experiments, results can be found here. At LP-Research, I program FPGAs, GPUs, MCUs and CPUs to process data coming from sources as varied as IMUs, video cameras and heart-rate monitors. I can assure you that particle physics is a perfectly valid qualification for this kind of work. Trust me, I'm a scientist.

Large-scale VR Application Case: the Holodeck Control Center

The AUDI Holodeck

Our large-scale VR solution allows any SteamVR-based (e.g. Unity, Unreal, VRED) Virtual Reality software to seamlessly use the HTC VIVE headset together with most large-room tracking systems available on the market (OptiTrack, Vicon, ART). It enables easy configuration and fits into the SteamVR framework, minimizing the effort needed to port applications to large rooms.

One of our first users, Lightshape, have recently released a video showing what they built with our technology.  They call it the Holodeck Control Center, an application which creates multi-user collaborative VR spaces. In it users can communicate and see the same scene whether they are the same real room or in different locations. The installation showcased in the video is used by German car maker Audi to study cars that haven’t been built yet.

Our technology is essential in order to get the best VR experience possible on the 15m × 15m of the main VR surface, combining optical tracking data and IMU measurements to provide precise and responsive positioning of the headsets.  Please have a look at Lightshape’s video below.

Ready for the HTC Vive Pro

In the near future, this installation will be updated to the HTC Vive Pro which our software already supports. The increased pixel density of this successor of the HTC Vive will make the scenes look even more realistic. The resolution is high enough to actually read the various panels once you are in the drivers seat! Besides that, we are also busy studying applications of the front-facing cameras of the Vive Pro in order to improve multi-user interaction.

Location-based VR Tracking for All SteamVR Applications

LPVR Pipeline Overview

UPDATE 1 – LPVR now offers VIVE Pro support!

UPDATE 2 – LPVR can now talk to all optical tracking systems that support VRPN (VICON, ART etc.).

UPDATE 3 – Of special interest to automotive customers may be that this also supports Autodesk VRED.

Current VR products cannot serve several important markets due to limitations of their tracking systems.  Out of the box, both the HTC Vive and the Oculus Rift are limited to tracking areas smaller than 5m x 5m, which is too small for most multi-player applications. We have previously presented our solution that combines our motion sensing IMU technology, OptiTrack camera-based tracking and the HTC Vive to allow responsive multiplayer VR experiences over larger areas. Because of the necessary interfacing this means that applications still need to be prepared specifically for this solution.

We have now further improved our software stack such that we can provide a SteamVR driver for our solution. What this means is that on the one hand any existing SteamVR application automatically supports the arbitrarily large tracking areas covered by the OptiTrack system. On the other hand it means that no additional plugins for Unity, Unreal or your development platform of choice is needed — support is automatic. Responsive behavior is guaranteed by using LP-RESEARCH’s IMU technology in combination with standard low-latency VR technologies like asynchronous time warping, late latching etc. An overview of the functionality of the system is shown in the image above.

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.