See-through Display First Look – LPVIZ (Part 3)

Virtual Dashboard Demonstration

This is a follow-up post to the introduction of our in-vehicle AR head mounted display LPVIZ part 1 and part 2.

To test LPVIZ we created a simple demo scenario of an automotive virtual dashboard. We created a Unity scene with graphic elements commonly found on a vehicle dashboard. We animated these elements to make the scene look more realistic.

This setup is meant for static testing at our shop. For further experiments inside a moving vehicle we are planning to connect the animated elements directly to car data (speed etc.) communicated over the CAN bus.

The virtual dashboard is only a very simple example to show the basic functionality of LPVIZ. As described in a previous post, many a lot more sophisticated applications can be implemented.

The video above was taken through the right eye optical waveguide display of LPVIZ. We took this photo with a regular smartphone camera and therefore it is not very high quality. Nevertheless, it confirms that the display is working and correctly shows the virtual dashboard.

The user is looking at the object straight ahead. In case the user rotates his head or changes position, his view of the object will change perspectively. An important point to mention is the high luminosity of the display. We took this photo with the interior lighting in our shop turned on normally, and without any additional shade in front of the display.

How to Use LPMS IMUs with LabView

Introduction

LabView by National Instruments (NI) is one of the most popular multi-purpose solutions for measurement and data acquisition tasks. A wide range of hardware components can be connected to a central control application running on a PC. This application contains a full graphical programming language that allows the creation of so called virtual instruments (VI).

Data can be acquired inside a LabView application via a variety of communication interfaces, such as Bluetooth, serial port etc. A LabView driver that can communicate with our LPMS units has been a frequently requested feature from our customers for some time, so that we decided to create this short example to give a general guideline.

A Simple Example

The example shown here specifically works with LPMS-B2, but it is easily customizable to work with other sensors in our product line-up. In order to communicate with LPMS-B2 we use LabView’s built-in Bluetooth access modules. We then parse the incoming data stream to display the measured values.

The source code repository for this example is here.

Figure 1 – Overview of a minimal virtual instrument (VI) to acquire data from LPMS-B2

Fig. 1 shows an overview of the example design to acquire the accelerometer X, Y, Z axes of the IMU and displays them on a simple front panel. Fig. 2 & 3 below show the virtual instrument in more detail. After reading out the raw data stream from the Bluetooth interface, this data stream is converted into a string. The string is then evaluated to find the start and stop character sequence. The actual data is finally extracted depending on its position in the data packet.

Figure 2 – Bluetooth access and initial data parsing

Figure 3 – Extraction of timestamp, accelerometer X, Y, Z values

Notes

Please note that the example requires manually entering the Bluetooth ID of the LPMS-B2 in use. The configuration of the data parsing is static. Therefore the output data of the sensor needs to be configured and saved to sensor flash memory in the LPMS-Control application. For reference please check the LPMS manual.

An initial version of this virtual instrument was kindly provided to us by Dr. Patrick Esser, head of the Movement Science Group at Oxford Brooks University, UK.

Collaboration with Pimax

We are happy to announce a collaboration with the head-mounted display (HMD) manufacturer Pimax. Pimax HMDs feature very high resolution (up to 8K pixels) displays and an industry-leading field-of-view (max. 200°). By default, Pimax HMDs support SteamVR tracking and therefore are limited to relatively small tracking volumes.

We developed a special driver that allows our LPVR middleware LPVR-CAD and LPVR-DUO to work with Pimax headsets. Using LPVR, the headsets can now be used within a large-scale, location-based context, in connection with outside-in optical systems such as ART (Advanced Real-Time Tracking).

As Pimax is planning to implement UltraLeap hand tracking in their HMDs in the future, we are confident that we will also be able to extend our inside-out tracking algorithm to their devices.

The video above shows the basic functionality of tracking a Pimax HMD using LPVR and an optical tracking system. The headset’s motions are represented in SteamVR. For this demonstration the tracking volume is relatively small, but can be extended easily by using more outside-in tracking cameras.

This video was kindly provided to us by evoTec Solutions. Evotec is a new company in Switzerland that focuses on virtual reality (VR) solutions for corporations. Contact them for further information!

Design Prototype and Inside-out Tracking – LPVIZ (Part 2)

LPVIZ Prototype Industrial Design

This post is a follow-up to the introduction of our augmented reality (AR) headset LPVIZ. See our previous post here.

For the past two months the LPVIZ team has been working hard to improve our initial prototype. We have enhanced the device’s appearance and optimized it ergonomically. My colleague Seeon Mitchel has made draft 3D prints of the design that he has been planning for the initial release of LPVIZ. The results are looking excellent (Figure 1 & 2).

The ring design for fixing the unit to the user’s head feels comfortable. Even for longer usage duration the unit does not cause fatigue to the neck. See below two photos of the current functional prototype with the newly printed shell.

Figure 1, 2 – The fully functional LPVIZ design prototype

Inside-out Tracking and Gesture Recognition

The latest LPVIZ prototype features a built-in stereo camera. We are using the excellent Rigel module by the company UltraLeap that allows us to, at the same time, run a SLAM (simultaneous localization and mapping) algorithm and UltraLeap’s hand tracking.

Using the Rigel’s stereo camera, my colleague Thomas Hauth has developed a state-of-the-art inside-out tracking algorithm that allows the headset to be used inside a vehicle, even if no special cameras are installed. The video (Figure 3) below shows the fundamental functionality of the algorithm.

Figure 3 – The video shows the fundamental functionality of the LPSLAM inside-out tracking algorithm

It is important to note that this will not be a full replacement for ART outside-in tracking inside the vehicle. ART’s tracking engine is more accurate and robust under difficult lighting conditions. Still, our purpose is to also serve customers that have a smaller budget or no possibility to install additional equipment inside their vehicle.

OpenZen 1.0 Release

Going Full Circle for Sensor Data Streaming with OpenZen

Since the foundation of LP-Research, it is not only important for us to provide excellent hardware to our customers but we also want to provide software components which ease the adoption and usage of our products. Over the years, we have provided various libraries to support customers using our sensor hardware on a diverse set of platforms.

As our range of sensor offerings is growing, we realized that we need to consolidate our software library stack while still supporting multiple platforms. We wanted to use this opportunity to create a more modular system to work with sensors with various measurement components.

Figure 1 – OpenZen Unity plugin connected to a LPMS-CU2 sensor and live visualization of sensor orientation.

Based on theses requirements, we developed OpenZen. It is our take on a high performance sensor data streaming and processing library. It combines our experience gained during mopre thant five years of sensor data processing with modern software techniques. The core of OpenZen is developed with the modern C++14 language. We are hosting the source code in an open source repository for seamless public domain access to learn and contribute to the code base.

Core Concept

One basic principle of OpenZen is to abstract the sensor components provided by a sensor from the transport layer of the communication. In this way, once the user is familiar with the OpenZen API, a wide range of sensor types via various connection layers can be used. To reach the lowest latency and the highest sensor data throughput, we designed OpenZen to be fully event-based and without any polling loops which could introduce delays.

Sensor Types and Connectivity

With release 1.0, OpenZen provides a sensor interface for the measurements of inertial-measurement units (IMU) and the output of global navigation satellite systems (GNSS). For example, our new LPMS-IG1P sensor is a combined IMU and GNSS-unit. Both units can be read-out via OpenZen.

A list of supported sensors is here.

OpenZen supports sensor connections via various interfaces like USB, serial port, CAN-Bus and Bluetooth. Furthermore, measurement data from sensors can also be streamed via a network and received on a second system by an OpenZen instance.

A list of supported transport layers is here.

Operating Systems and Programming Languages

Currently, OpenZen can be compiled and used on Windows, Linux and MacOS systems. We are working on ports to more platforms, for example Android. Due to its modular design, the OpenZen API can be accessed from many programming languages. At this time, we support the C, C++ and C# programming languages and we provide a ready-to-go Unity plugin.

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