About Klaus Petersen

I like to create magical things, especially projects related to new technologies like augmented and virtual reality, mobile robotics and MEMS-based sensor networks. I code in C(++) and Python, trying to keep up with my very talented colleagues :-)

FusionHub – Sensor Fusion Operating System


In the past years our team at LP-RESEARCH has worked on a number of customer projects that required us to customize our sensor fusion algorithms for a specific application.

Such customization usually doesn’t include significant changes to our existing filter algorithms, but in most cases only warrants changes to input/output interfacing, specialized extensions to the core functionality.

Our own products such as the LPMS inertial measurement units, the LPVR virtual and augmented reality tracking system series and LPVIZ are based to a large extent on the same fundamental algorithms, but with different sensor inputs and different data output interfaces.

For this reason we decided to develop a modular platform that would allow us to create a graph of nodes, each node with a specific functionality as either:

  • Sensor data source (Gyroscope source, GPS etc.)
  • Filter algorithm (IMU-optical filter, odometry-GPS filter etc.)
  • Output sinks (File writer, websocket output, VRPN output etc.)

Modular Platform

This modular platform we call the FusionHub or sensor fusion operating system (SFOS) as it creates an end-to-end solution for applications that need a high performance and flexible sensor input/output and filtering system. FusionHub is platform independent and can run on Windows, Linux and Android. We are also working on a port to Apple’s VisionOS.

One might argue that a very simlar solution exists in the form of the Robot Operating System (ROS). ROS is a powerful platform, but it is relatively large in the amount of base components that it needs to run.

With FusionHub, we rely on the ZeroMQ protocol and Google’s Protocol Buffers for the communication of our nodes. This keeps the application light weight and independent from any other software stack or platform.

Apart from proprietary external libraries that might be needed to connect to certain sensor systems, FusionHub is a standalone application. It easily runs even on embedded hardware systems such as the Meta Quest virtual reality display series.

Easy Configuration

The components of a FusionHub graph are configured using a simple JSON script. For example a sensor input from one of our LPMS-IG1 IMUs is configured as shown below:

An IMU-optical sensor fusion node would be configured similar to the below script:

Each node contains input and output endpoints, or one of the two for source and sink nodes, that allow other nodes to connect to them. Connections can be limited to inside the application (“inproc://..”) or also be accessible from outside the application (“tcp://..”). This allows for constructing node networks that run on distributed machines. For example one computer could acquire data from a specific sensor and another computer could apply a sensor fusion algorithm to the incoming data.

This is also a simple way how additional nodes developed by 3rd parties can communicate with or via the FusionHub.

We have so far created the follwing node components:


  • LPMS input node
  • Optical tracking system input node (ART, Optitrack, VICON, Antilatency)
  • (RTK-)GPS input nodes (NMEA, RTCM)
  • Car odometry via CAN bus input node


  • File writer node
  • VRPN output node
  • Websocket output node


  • IMU-optical fusion node (for LPVR systems)
  • GPS-odometry fusion node (for car navigation)
  • RTK-GPS-odoemtry-IMU fusion node (for car navigation)

Future Outlook

With great success we’ve deployed FusionHub for our own products as well as several customer projects.

While our collection of node components and the range of applications for FusionHub is growing, we spend significant development resources on creating testing frameworks that guarantee the performance and robustness of FusionHub. As the application of FusionHub in our LPVIZ driving assistance system is mission critical, reliability and redundancy of our core framework is essential.

In the future we’re looking to connect FusionHub with our internal IoT solution LPIOT to open up access to our core algorithm to large numbers of sensor devices in parallel. Furthermore we are working with machine learning-focused company Archetype AI to give deeper inteligence to this solution.

See further information about how to use FusionHub in the FusionHub documentation.

LPVR-AIR for Immersive Collaborative Industrial Design

Wireless Content Streaming

LPVR-AIR is LP-Research’s wireless VR streaming solution. Content is generated on a rendering computer and wirelessly streamed to a VR headset to be displayed. At the same time the pose, orientation and position, of the headset is calculated from tracking data from a camera system and inertial measurements on the headset itself.

The core tracking algorithm of LPVR-AIR is similar to our LPVR-CAD solution. We are combining this established tracking method with wireless data streaming.

This has a few significant advantages:

  • Rendering detailed VR content is computationally too heavy to do all calculations on embedded hardware on the headset itself. Therefore, content needs to be rendered on an external computer and the result is streamed to the headset. LPVR-AIR allows doing this.
  • Designers in eg. the automotive space have their own preference of applications to create content, such as Autodesk VRED. These applications usually don’t run on a headset’s embedded hardware. With LPVR-AIR dsigners can use any application that normally works with a Windows based PC.

Technical Implementation

See below a block diagram of how the LPVR-AIR system is implemented. While we in principle support any Android based standalone VR headset, we currently focus on the Meta Quest line of HMDs, specifically Meta Quest Pro and Meta Quest 3.

Our solution effectively enables designers to explore a large 3D design space with full high resolution renderings using a lightweight headset. LPVR-AIR even allows for the interaction of several users in a design space. An example of such a use case is shown in the video on the top of this post. Two users in our office in Tokyo, being tracked by LPVR tracking, explore a car design together.

Improved Design Process

This opens new possibilities for automotive, industrial, architecture and many more design applications, leading to increased performance of designers and a higher success rate of their designs. LPVR-AIR is based on the ALVR wireless streaming engine, which we have extended to work with our FusionHub sensor fusion solution.

Long term, the ALVR engine makes it easy for us to support a number of different HMDs, additionally to Meta Quest also the Varjo and eventually the Apple Vision Pro series as shown in the image below. With VRED we have an outstanding rendering solution at the base of LPVR-AIR that allows designers to create photo-realistic content while providing extensive collaboration abilities.

If you would like to move towards immersive and interactive 3D design, don’t hesitate to consult with us and give our LPVR-AIR on-premise collaborative design solution a try!

Immersive Driving Assistance with LPVIZ

How LPVIZ Augments Driving Reality

Going beyond a simple screen replacement, LPVIZ is an augmented reality driving assistance solution for the car. It allows displaying related content to a driver or passenger in 3D, superimposed to reality. Content can be placed anywhere inside the car, such as a virtual speedometer over the dashboard, and anywhere outside of the car, such as point-of-interest markers or navigation guidance.

The video on top of this post shows what a drive around the block in Azabujuban, Tokyo with LPVIZ looks like. A virtual dashboard is projected onto the center console of the vehicle. Arrows on the ground show lane guidance to the driver. Red Google Maps-style markers show points of interest. The virtual dashboard stays fixed to the same location in the car, even when the vehicle turns. The navigation arrows move smoothly and the point-of-interest markers are globally anchored.

Perfectly Tuned Components

LPVIZ consists of several components that all have to interact perfectly to create a compelling and safe augmentation experience. The below illustration shows a block diagram of how the hardware components are connected.

Accurate tracking is required to display useful content to the driver: the HMD pose in the local car coordinate system and the vehicle pose in a globally anchored frame. Precise calibration of all components of the solution is essential to provide the highest visual fidelity and driver safety. Our LPVIZ product makes all parts of the system available in a compact form factor, ready to be integrated with any vehicle.

The Past, Present and the Future

In the current development stage we’re focusing on the most essential aspects of the solution: displaying a virtual dashboard, navigation information and points-of-interest. While this is our proprietary content, we’re opening our software to work with 3rd party developers to create their own content building on our platform.

Currently we’re offering LPVIZ as a B2B solution for prototyping, design and research. However, we’re working on reducing system complexity to make it work as a consumer facing automotive after-market solution to be released later this year.

Towards a Consumer Product

We are very proud of the progress our team has made in the past months. We’re moving closer to making our vision of an augmented reality driving assistance system a reality for everyone. One very important take-away from our recent developments is that it’s indeed possible to provide real utility to the driver using technology that is readily available. It might still be early days, but we’re edging towards a product that could appeal to a wider consumer market. This is just the beginning.

High-performance Use Cases of LPVR & Varjo Headsets

Components of a VR/AR Operating System

Augmented and virtual reality technology helps boost worker productivity in various fields such as automotive, aerospace, industrial production, and more. Whereas the context of these applications is usually fairly specific, some aspects are common to many of these use cases. In this article, we will specifically explore the topic of pose tracking of Varjo head mounted displays (HMDs) based on LP-RESEARCH’s LPVR operating system. We will further on show two customer use cases that utilize LPVR in different ways.

In a typical VR/AR setup, you find three main subsystems as shown in the illustration below:

With our LPVR operating system, we connect these three building blocks of an VR/AR system and make them communicate seamlessly with each other while providing a simple, unified interface to the user. Depending on the specific use case, users might select different types of hardware to build their VR/AR setup. Therefore LPVR offers a wide range of interface options to adapt to systems from various manufacturers.

LPVR Flavors

LPVR operates in different flavors, we can group end applications into two categories:

  • LPVR-CAD – Static AR/VR setups, where multiple users operate and collaborate in one or more joint tracking volumes. These tracking volumes can be situated in different locations.
  • LPVR-DUO – AR/VR systems that are located in a vehicle or on a motion platform: such systems have special requirements, especially on the tracking side. If, for example, you would want to track a headset inside a car, displaying a virtual cockpit anchored to the car frame, and a virtual outside world fixed to a global coordinate system, means of locating the car in the world and referencing the HMD locally in the car frame are required.


In the following paragraphs, we will introduce two customer use cases that cover these two basic scenarios.

Large-scale Industrial Design at Hyundai

– Varjo XR-3 at Hyundai Design Center with optical markers attached. Image credit: Hyundai

For the Korean automotive company Hyundai Motor Company, we created a large, location-based virtual reality installation at their research and development center in Namyang, Korea. The system is used to showcase, amend and modify prototype and production-ready automobile designs.

This application uses optical outside-in tracking and LP-RESEARCH’s LPVR-CAD solution to track up to 20 users wearing head-mounted displays. While LPVR allows a mix of different headset types to operate in the same tracking volume, the Varjo XR-3 gives the most outstanding performance to inspect objects in high resolution and great detail. Additionally to an HMD, users carry hand controllers for a total of more than 40 tracked objects in a space close to 400 sqm.

– Hyundai’s collaborative virtual reality design experience. Image credit: Hyundai

Responsiveness is achieved by using LPVR-CAD to combine data from the inertial measurement unit built into the headsets and information from the optical tracking system. The optical system uses 36 infrared cameras to track the 160 markers attached to the HMDs and hand controllers. Perfectly smooth and uninterrupted position and orientation data of each user’s HMD is achieved by using LP-RESEARCH’s sensor fusion algorithms.

Depending on the type of headset, users either wear a backpack PC, connect to a host wirelessly or use an extra-long cable to connect directly to a rendering PC outside the tracking volume.

“Currently, we are actively utilizing VR from the initial development stage to the point of development. In the future, we plan to increase accessibility and usability by simplifying equipment using wireless HMDs. For this, improving the speed and stability of wireless internet is essential, which we plan to address by introducing 5G. In addition, LP RESEARCH’s technology is essential for multi-user location sharing within a virtual space.” – SungMook Kang, Visualization Specialist, Hyundai Motor Company

Next-level Automotive Entertainment with CUPRA

Imagine.. playing Mario cart, your hands are gripping the wheel, and you are in Neo Tokyo, on a race track. Futuristic buildings keep flying by while you race ahead, drifting into long turns and leaving your competitors behind you.

Now imagine you are no longer in your living room, you are sitting in an actual race car, buzzing around in an empty parking lot. Instead of looking through the windshield with your own eyes, you are wearing a Varjo XR-3 HMD. What you see outside the car is a virtual world, it’s Neo Tokyo.

– The view through the Varjo XR-3 headset. Image credit: CUPRA

As the car moves on the parking lot, you move inside the virtual world. When you move your head inside the car’s cockpit, the motions of your head are accurately tracked.

– Varjo XR-3 inside the cabin of the Urban Rebel. Image credit: Fonk Magazine

– Cupra’s Urban Rebel drifting on the test course

Together with the Norwegian company Breach VR, we have implemented this experience for the automotive company CUPRA. CUPRA is relentlessly pushing the technology of their vehicles into the future, striving to provide a novel driving experience to their customers.

Tracking of the vehicle and the Varjo XR-3 inside the vehicle is achieved with LP-RESEARCH’s automotive tracking systems LPVR-DUO. As the headset’s gyroscope sensors record the superimposed motion data of the car and the user inside the car, a specialized sensing system, and the algorithm are required to separate the two.

The result of this cascade of exceptional technology is a compellingly immersive driving experience of the future. The combination of an outstanding visualization device like the Varjo XR-3, LPVR state-of-the-art tracking, BreachVR’s 3D software and design and, last but not least, the incredible CUPRA race cars make for an exciting ride that you’ll greatly enjoy and never forget. Come and join the ride!

Check this blog blog post in the Varjo Insider Blog.

Check out our Instagram for further use cases with Varjo’s HMDs: @lpresearchinc

Exploring Affective Computing Concepts


Emotional computing isn’t a new field of research. For decades computer scientists have worked on modelling, measuring and actuating human emotion. The goal of doing this accurately and predictively has so far been elusive.


In the past years we have worked with the company Qualcomm to create intellectual property related to this topic, in the context of health care and the automotive space. Even though this project is pretty off-topic from our ususal focus areas it is an interesting sidetrack that I think is worth posting about.

Affective Computing Concepts

As part of the program we have worked on various ideas ranging from relatively simple sensory devices to complete affective control systems to control the emotional state of a user. Two examples of these approaches to emotional computing are shown below.

The Skin Color Sensor measures the color of the facial complexion of a user, with the goal of estimating aspects of the emotional state of the person from this data. The sensor is to have the shape of a small, unobtrusive patch to be attached to a spot on the forehead of the user.

Another affective computing concept we have worked on is the Affectactic Engine. A little device that measures the emotional state of a user via an electromyography sensor and accelerometer. Simply speaking we are imagining that high muscle tension and certain motion patterns correspond to a stressed emotional state of the user or represent a “twitch” a user might have.

The user is to be reminded of entering this “stressed” emotional state by vibrations emitted from the device. The device is to be attached to the body of the user by a wrist band, with the goal of reminding the user of certain subconscious stress states.


In the course of this collaboration we created several groundbreaking patents in the area of affective computing:

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