LPVR-DUO in an Airborne Helicopter

In-Flight VR

Imagine soaring through the skies as a pilot, testing the limits of a helicopter’s capabilities while feeling the rush of wind and turbulence. Now imagine that you don’t see the real world outside and the safe landing pad that your helicopter is approaching but a virtual reality (VR) scene where you are homing in on a ship in high seas. The National Research Council Canada (NRC) and Defence Research and Development Canada (DRDC) have brought this experience to life with their groundbreaking Integrated Reality In-Flight Simulation (IRIS).

IRIS is not your ordinary simulator; for one, it’s not sitting on a hexapod, it’s airborne. It’s a variable-stability helicopter based on the Bell 412 that can behave like other aircraft and can simulate varying weather conditions; combine that with a VR environment and you have a tool that allows safely training operations in the most adverse conditions. In particular it is used for Ship Helicopter Operating Limitations (SHOL) testing.

Mission-Critical Application with LPVR-DUO

The LPVR-DUO system is what makes VR possible on this constantly moving platform. This cutting-edge AR/VR tracking system seamlessly merges the inertial measurements taken by the headset with the helicopter’s motion data and a camera system mounted inside the cabin to provide the correct visuals to the pilot. The challenges of using cameras to track the VR headset inside the tight environment of the helicopter while lighting conditions are ever-changing are overcome by using an ART SmartTrack 3 system. This system follows an arrangement of reflective markers attached to the pilot’s helmet. The VR headset is attached to the helmet in such a way that the pilot can wear it as if it were a pair of night vision goggles. Put together, this allows displaying a virtual world to the pilot, even in the most extreme maneuvers.

To ensure an authentic experience, the IRIS system incorporates real-time turbulence models, meticulously crafted from wind tunnel trials. These turbulence effects are seamlessly integrated into the aircraft’s motion and into the VR scene, providing pilots with precise proprioceptive and vestibular cues. It’s a symphony of technology and innovation in the world of aviation testing.

In-Cockpit Implementation

The optical tracking system relies on highly reflective marker targets on the helmet to track movement in three dimensions. Initially, only five markers were installed, strategically placed for optimal tracking. But the pursuit of perfection led NRC to create custom 3D-printed low-reflectivity helmet molds, allowing them to mount a dozen small passive markers. This significantly improved tracking reliability in various lighting conditions and allowed for a wider range of head movement.

Recently, NRC put this remarkable concept to the test with actual flight trials. The response from pilots was nothing short of exhilarating. They found the system required minimal adaptation, exhibited no noticeable lag, and, perhaps most impressively, didn’t induce any motion sickness. Even the turbulence effects felt incredibly realistic. Surprisingly, the typical VR drawbacks, such as resolution and field of view limitations, had minimal impact, especially during close-in shipboard operations. It’s safe to say that IRIS has set a new standard for effective and immersive aviation testing.

Publication of Results

The NRC team presented their results at the Vertical Flight Society’s 79th Annual Form in two papers [1]  and [2] and they also have a blog post on their site.

NOTE: Image contents courtesy of Aerospace Research Centre, National Research Council of Canada (NRC) – Ottawa, ON, Canada

Exploring Affective Computing Concepts

Introduction

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.

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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.

Patents

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

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.

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.

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.

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