Introduction

FusionHub is a software application that has the purpose of combining a number of sensor data inputs to create a higher level information output. There are 3 basic versions of FusionHub:

• FusionHub BASE combines data from an outside-in tracking system with inertial measurements done by an inertial measurement unit (IMU). Typical applications: Head-mounted display tracking for VR/AR applications, camera tracking for virtual production

• FusionHub MOVE adds an additional platform IMU to the BASE configuration. It combines data from both IMUs to calculate poses relative to a moving platform. Typical applications: AR/VR in a vehicle, aircraft, or on a simulator platform

• FusionHub FLOW combines odometry, GPS and IMU data from a vehicle to calculate high-accuracy and low-latency global localization information. Typical applications: Automobile localization, robot localization

The diagram below shows the general structure of FusionHub. Sources and sinks are connected by a filter unit. The sensor fusion functionality is contained in this filter unit. The filter parameters as well as the parameters of input and output blocks can be configured via a configuration script or the graphical user interface.

The graphical user interface is detached from the main FusionHub application and both applications can therefore run on separate computers. This provides flexibility for running FusionHub on devices with limited monitoring capabilities like a head mounted display.

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General

Starting FusionHub

FusionHub consists of two components:

• The main application

• A graphical user interface application

Insert the security dongle into a USB port of your computer.

The main FusionHub application is started by running FusionHub.exe. No specific installation is needed, the application can be run directly out of its deployment directory. It is a command line application that uses the file config.json for its configuration. We will explan the contents and options of the configuration file further below.

Please install the graphical user interface by running lp-fusionhub-dashboard_0.1.0_x64_en-US.msi. It installs lp-fusionhub-dashboard in your start menu, launch the application from there. Press the Connect button after starting FusionHub.exe to connect client and server. In case you are running FusionHub on a separate machine make sure to enter the correct IP address.

The screenshot below shows the connection elements of the GUI.

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Licensing

FusionHub has two options for license protection:

Hardware dongle

License authentication using a hardware dongle; This is especially interesting for air-gapped installations that are not connected to the internet. As long as the dongle is inserted into a USB slot of the host system, FusionHub will run. Please note that for the Android (Quest 2 HMD) version of FusionHub, the GUI running on the streaming host is dongle protected, see more detailed information in the specific manual chapter.

Online license

License authentication using a software, online license; This makes sense for systems that are connected to the internet at least during the initial installation of FusionHub. The software checks its license status with our license server with following sequence:

1. Enter license key in configuration file. You receive your personal license key from us.

2. Send license key and machine code to server

3. Server checks if license is valid and returns response code, if it is valid

4. Copy the response code from the log and enter it in the config file to the ResponseKey parameter. Save the config file.

5. This allows FusionHub to run on this specific machine without reconnecting to the internet. One license unit will be subtracted from your license account. Please ask us for assitance if you’d like to move your license.

If your default configuration file config.json doesn't contain it already, add the LicenseInfo block as shown below. Enter your personal key you received from us as LicenseKey.

BASE Filter Configuration

FusionHub BASE combines data from an outside-in tracking system with inertial measurements by an inertial measurement unit (IMU). The BASE filter integrates the angular velocity measured by the IMU’s gyroscope and corrects it by the pose of the optical target that is determined with the optical tracking system. This references the calculated pose to the coordinate system of the optical tracking system and avoids drift while maintaining the high frequency and responsiveness of the gyroscope data. The diagram below shows an overview of a BASE filter system.

The position output of the BASE filter is transferred directly from the optical measurements without modification. Pose prediction and interpolation of position measurements by accelerometer integration are under development.

Setup

• Setup your optical tracking system. Attach the IMU to the optical target or attach both to the same rigid object eg. an HMD. Initialize the optical tracking body in your motion capture software and note the object ID.

• Connect your IMU to the computer running FusionHub. Make sure your computer can connect to the IMU and read data by using LpmsControl 2. Make sure to disconnect from LpmsControl before running FusionHub.

• Modify config.json to contain the correct information for your IMU and optical tracking system. See below how to configure the blocks in the configuration file. The configuration file can also be modified through the FusionHub GUI as shown further below.

Operation

If all components are connected and the configuration file is valid, FusionHub should work right away after starting the application. The console output shows a log of the initialization of the various components. Note that you can log the output from FusionHub to a file by adding

to the sink section of config.json.

After starting and connecting the GUI the Auto Calibration section of Fusion Config should show increasing numbers for nImu (number of recorded IMU samples) and nOptical (number of recorded optical samples).

Calibration

There are two calibration steps that are required to operate the BASE filter:

Gyroscope Autocalibration

Gyroscope sensors have a built-in measurement bias that changes over time and is temperature-dependent. Good, permanent temperature calibration of MEMS gyroscopes is hard to achieve, therefore FusionHub offers the possibility to run-time calibrate this offset. This calibration is semi-automatic.

The measurement bias of the gyroscope attached to the tracked object is calculated as an average of the data acquired over a certain time interval. Requirement for this sampling to happen is for the object to be in a non-moving / static state. The state of the object is determined by input data from the optical tracking. So once the optical tracking system (eg. ART DTrack) reports the optical target to be static, gyroscope data will be sampled, averaged and a new bias compensation vector calculated.

The result of the autocalibration is saved in autocalibValue.json. When starting FusionHub for the first time, this offset is set to (0, 0, 0). Make sure to place the target, with the IMU attached, within the tracking volume and keep it static eg. by putting it on the floor.

IMU-Optical Intercalibration

The IMU-optical intercalibration calibrates the orientation difference between IMU and optical tracking body. When setting up a new system or after modifying the optical target a (re-)calibration is needed. The calibration is started by running FusionHub with the runIntercalibration option set to true.

Rotate the target with the IMU attached slowly within the tracking volume. You can monitor the status of the intercalibration in the Intercalibration section on the Fusion Config page of the GUI. After around 50 sampled poses the intercalibration should be finished and the GUI should show the resulting calibration quaternion.

Click Apply Intercalibration Result to automatically insert the result into the configuration file. Click Set and Save at the bottom of the editor to save the result and restart FusionHub.

Check the 3D View page to confirm if the intercalibration result is correct. The red and white cube should overlap alpmost exactly at all times when you rotate your object inside the tracking volume. Note that after a restart it might take a few seconds for optical and fused pose to converge.

IMU-Optical Fusion Filter

Example Configuration

Real-time IMU-optical fusion with LPMS-IG1 and ART Dtrack: imuOpticalFusion.json

Configuration Block

Node name: fusion

This filter needs as input:

• Optical tracking source

• IMU source

This Filter outputs:

• fusedPose

Output Data Format

Source Options

Optical Tracking Source Options

Advanced Realtime Tracking (ART)

FusionHub works with all ART tracking systems, based on their DTrack tracking software.

Optitrack

FusionHub works with all Optitrack tracking systems based on their Motive tracking software.

VICON

FusionHub consumes VICON’s DataStream protocol. Communication has been tested with their Shogun software.

Antilatency

FusionHub connects directly to Antilatency’s USB or wireless trackers.

IMU Source

FusionHub supports all LP-RESEARCH IMUs.

See a description of how to prepare LPMS-IG1 for operation with FusionHub further below.

LPMS-IG1

LPMS-CURS3

Graphical User Interface

Dashboard

3D Viewer

Sensor Fusion Configuration and Calibration Status

General Settings

MOVE Filter Configuration

FusionHub MOVE adds an additional platform IMU to the BASE configuration. It combines data from both IMUs to calculate poses relative to a moving platform.

The MOVE filter section of FusionHub is still under development. Refer to LPVR-DUO for an implementation of the filter for specific virtual / augmented reality headsets.

FLOW Filter Configuration

FusionHub FLOW combines odometry, GPS and IMU data from a vehicle to calculate high-accuracy and low-latency global localization information. While GPS or RTK-GPS measurements alone provide similar positioning accuracy the output frequency of these systems is relatively low, making them unsuitable for applications where localization information at higher framerates is required, such as positioning objects in an augmented reality environment.

By additionally using odometry (wheel speeds, steering angle etc.) information, the localization data from the GPS measurements is interpolated to achieve framerates limited only by IMU and odometry sampling speeds.

The FLOW filter has two operation modes with different configuration blocks in config.json and different output formats. The two modes are:

• Low-dynamics filter (LD)

• High-dynamics filter (HD)

The diagram below shows an overview of a simple FLOW filter setup.

Installation of Hardware Components

Inertial Measurement Unit (IMU)

LPMS-IG1P needs to be installled in the vehicle in a known orientation ideally with the coordinate axes of the IMU arranged in parallel to the vehicle coordinate system. As vehicle reference frame we are using the VW coordinate system as shown in the image below. Connect the USB connector of LPMS-IG1P to the host computer. If needed an active or passive USB extension can be used. Make sure to check data integrity with the LpmsControl 2 data acquisition tool, we have noticed communication issues with some passive USB extensions.

Global Positioning System (GPS)

The GPS receiver is integrated with the LPMS-IG1P sensor. Connect the antenna cable and place the GPS antenna on top of the vehicle.

Alternatively, a standalone RTK gps module can be used as a gps input source as well.

CAN Bus Connection

FusionHub can be connected to the vehicle CAN bus by using one of the following CAN bus interfaces:

• PCAN USB interface

• Vector CAN USB interface

Low-dynamics Filter (Odometry + GPS + (some) IMU)

Configuration Block

Node name: vehicularFusion

This filter needs as input:

• LPMS-IG1P data source for IMU and GPS data

• Alternatively for case with separate IMU and RTK GPS sources (with NTRIP Caster for RTK correction)

RTCM Source

GNSS Source

• CAN bus and vehicle decoder source

This Filter outputs:

• fusedVehiclePose

Output Data Format

Additional Notes

The FusedVehiclePose contains a 3D acceleration vector. The acceleration is defined in the following manner: There's a configuration flag imuToCarRotation which takes a quaternion used to rotate vectors in the IMU frame to the car frame. By default it is the identity quaternion. For the LD model, the measured IMU acceleration is simply rotated by the imuToCarRotation and written to the output.

In the LD filter, pitch and roll has to be derived from the acceleration data based on a model of the stiffness of the chassis. That assumes a flat surface. The HD model offers the full 6-DOF, and we are planning to unify them to have all data available at all times.

As the filter relies heavily on GPS measurements it doesn’t deliver good results indoors. The better GPS reception, the better the resulting output of the filter. The yaw angle of the vehicle is calculated based on several GPS and odometry measurements when the car is moving. Therefore, after starting FusionHub, while the car is static, the filter will not deliver a correct yaw angle. The angle will be adjusted to the correct direction after a few seconds of driving the vehicle.

Example Configuration

Playback and fusion of prerecorded data: gpsImuFusionPlayback.json

Real-time fusion: gpsOdometryFusion.json

High-Dynamics Filter (IMU + GPS)

Node name: gnssImuFusion

Configuration block example (in sinks section)

Setting up the ImuToCarRotation parameter

The used car frame is VW coordinate frame,

The IMU sensor can be mounted in any way but the ImuToCarRotation quaternion need to be provided to transform the IMU data into VW frame.

Example

If the IMU is mounted like follows,

To match the VW frame, we need a 180° rotation around the z axis (clockwise). Therefore, the rotation matrix would be,

And the orientation quaternion woud be [x, y, z, w] = [ 0, 0, 1, 0 ] which can be specified in the configuration like below,

This filter needs as input:

• LPMS-IG1P data source for IMU and GPS data

• Alternatively for case with separate IMU and RTK GPS sources (with NTRIP Caster for RTK correction)

RTCM Source

GNSS Source

CAN bus and vehicle decoder source

This Filter outputs:

• fusedVehiclePose (2D pose): Output equivalent to the LD filter output. Includes position in meters relative to starting point, global position (lon, lat) and heading.

• fusedPose (3D pose): relative to starting point, x, y (in meters) + z (height) + 3D orientation quaternion

• globalFusedPose: globally referenced 3D position (longitude, latitude, height) + 3D orientation quaternion in ENU frame

Output data format

FusedVehiclePose