Page Comparison

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Inertial Measurement Unit (IMU)

LPMS-IG1P needs to be installled installed 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 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.

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• PCAN USB interface

• Vector CAN USB interface

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

The low dynamics (LD) filter combines odometry, GPS and IMU data to calculate the global position of a vehicle. This filter was initially concipated to work only with odometry and GPS data to calculate the 2D position and yaw angle of a vehicle. It is therefore suitable for simple tracking scenarios. For more complicated, for example augmented reality, applications we recommed using the HD filter as it outputs globally referenced 3D orientation additionally to globally referenced position. This filter relies heavily on the wheel velocity data of the car published on the car’s CAN bus. Therefore the quality of the output of this filter depends on the accuracy of this information.

Configuration Block

Insert the following configuration block into your config.json file to activate the LD filter. The filter's node name is vehicularFusion.

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LPVR-POS Sensor Fusion Filter

Filter Inputs

Both the LD and the HD filter need the following sources as input. One option is to operate the filter with our LPMS-IG1P sensor that contains IMU and a GPS receiver. This allows for standard GPS absolute position accuracy. Relative accuracy and update rate are higher, based on odometry and IMU data. The other option is to use a separate RTK-GPS unit to get high accuracy RTK-GPS readings. We will look at how to set up an RTK-GPS system for LPVR-POS in a following chapter.

Option 1 - LPMS-IG1P data source for IMU and GPS data

LPMS-IG1P Source

"imuP": {
    "echoFusedPosetype": false,
    "endpoint": "tcp://*:8801"DualRtk",
    "fusersettings": {
        "fitModelsensor1": "SimpleCarModel", {
          "driveModel": "Differential",
        "velError": 0.277777778,  // If specification needed, insert first IG1 sensor name here
            //"omegaErrorname": 0.5,"ig1p232800650050",
            "measurementErrorautodetectType": 0.1,"ig1p"
        "smoothFit": true},
        "useImuTurnRatertcm": true,
        "imuTurnRateAxisimuEndpoint": {
            "x": 1,"tcp://*:8802"
             "y": 0,
            "z": 0
        }
    }
}

See below a description of the parameter options of the LD filter.

...

Parameter name

}
}

Filter Inputs

This filter needs the following sources as input. One option is to operate the filter with our LPMS-IG1P sensor that contains IMU and a GPS receiver. This allows for standard GPS absolute position accuracy. Relative accuracy and update rate are higher, based on odometry and IMU data. The other option is to use a separate RTK-GPS unit to get high accuracy RTK-GPS readings. We will look at how to set up an RTK-GPS system for LPVR-POS in a following chapter.

Option 1 - LPMS-IG1P data source for IMU and GPS data

LPMS-IG1P Source

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Option 2 - Separate LPMS-IG1 IMU and RTK GPS sources (with NTRIP caster for RTK correction)

LPMS-IG1 Source

"imu": {
  "type": "OpenZen",
  "settings": {
    "autodetectType": "ig1"
  }
}

RTCM Source

"RTCM": {
    "type": "DualRtkNTRIP",
    "settings": {
        "sensor1host": {"some-host-name",
        "port": "2101",
     //  If specification needed"mountpoint": "some-mount-point",
insert first IG1 sensor name here   "user": "some-user",
        //"namepassword": "ig1p232800650050some-password",
 
          "autodetectTypeuserAgent": "ig1pLPVR",
         }"initialLatitude": 35.65736,
        "rtcminitialLongitude": true139.73239,
        "imuEndpointforwardGnss": "tcp://*:8802" true
    }
}

Option 2 - Separate LPMS-IG1 IMU and RTK GPS sources (with NTRIP caster for RTK correction)

LPMS-IG1 Source

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GNSS Source

"gnss": {
    "type": "OpenZenNMEA",
    "settings": {
        "autodetectTypeport": "ig1"/dev/ttyUSB0",
       }
}

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Parameter name

...

Description

...

Default

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type

...

Type of IMU. At the moment only OpenZen IMUs are supported.

...

OpenZen

...

name

...

The name of the LPMS-IG1 sensor used in this setup. This parameter is optional. If FusionHub is operated at the same time with LPVR-DUO, we recommend specifying the sensor name. Look up the sensor name in LpmsControl 2.

...

n/a

...

autodetectType

...

Type of sensor to be autodetcted

...

ig1

...

imuEndpoint

...

Output endpoint of IMU data. This parameter is optional.

...

tcp://*:8802

RTCM Source

"RTCM "baudrate": 115200,
        "rtcm": true
    }
}

CAN bus and vehicle decoder source

"vehicle": {
    "type": "NTRIPAutomotive",
    "settingsvehicleStateEndpoint": {
        "host": "some-host-name",
  "tcp://*:8999",
     "portsettings": "2101",{
        "mountpointcanInterface": "some-mount-pointPeakCAN",
        "uservehicleType": "some-userR56",
        "password": "some-password",
        "userAgent": "LPVR",
        "initialLatitude": 35.65736,
        "initialLongitude": 139.73239,
        "forwardGnss":  true
    }
}

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Parameter name

...

Description

...

Default

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type

...

Type of RTCM correction data source. Currently only NTRIP is allowed.

...

NTRIP

...

host

...

NTrip caster host.

...

192.168.1.1

...

port

...

NTrip caster port.

...

2101

...

mountpoint

...

NTrip mountpoint or stream to receive rtcm correction data.

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user

...

NTrip caster username.

...

password

...

NTrip caster password.

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userAgent

...

Name of user agent when connecting to NTrip caster.

...

LPVR-POS

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initialLatitude

...

Latitude to forward to Ntrip caster on first connect.

...

0.0

...

initialLongitude

...

Longitude to forward to Ntrip caster on first connect.

...

0.0

...

forwardGnss

...

Set true if gnss data from gnss source is to be forwarded to NTRIP caster. This is useful if Ntrip caster offers dynamic switching of RTCM correction data based on forwarded location.

...

false

GNSS Source

"gnss": {
    "type": "NMEA",
    "settings": {
        "port": "/dev/ttyUSB0",
        "baudrate": 115200,
        "rtcm": true
    }
}

...

Parameter name

...

Description

...

Default

...

type

...

Data output format for gnss data source. Currently only NMEA is allowed.

...

NMEA

...

port

...

Serial port number for gnss source.

...

baudrate

...

Serial port baudrate to connect to gnss source.

...

rtcm

...

Set true to enable RTCM correction data forwarding from RTCM source to gnss module.

...

false

CAN bus and vehicle decoder source

"vehicle}
}

Low-dynamics Filter

General

The low dynamics (LD) filter combines odometry, GPS and IMU data to calculate the global position of a vehicle. This filter was initially concipated to work only with odometry and GPS data to calculate the 2D position and yaw angle of a vehicle. It is therefore suitable for simple tracking scenarios. For more complicated, for example augmented reality, applications we recommed using the HD filter as it outputs globally referenced 3D orientation additionally to globally referenced position. This filter relies heavily on the wheel velocity data of the car published on the car’s CAN bus. Therefore the quality of the output of this filter depends on the accuracy of this information.

Notes on Data Output

The LD filter outputs fusedVehiclePose. 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.

Notes on IMU Arrangement

LPMS-IG1P needs to be installed in the vehicle in a known orientation ideally with the coordinate axes of the IMU arranged in parallel to the vehicle coordinate system. The LD filter uses the imuTurnRateAxis parameter to determine which axis it should use to calculate the vehicle’s orientation. For example if the IMU is installed in the car so that the Z axis is pointing upwards, imuTurnRateAxis should be set to 0, 0, 1.

Configuration Block

Insert the following configuration block into your config.json file to activate the LD filter. The filter's node name is vehicularFusion.

"vehicularFusion": {
    "typeechoFusedPose": "Automotive"false,
    "vehicleStateEndpointendpoint": "tcp://*:89998801",
    "settingsfuser": {
        "canInterfacefitModel": "PeakCANSimpleCarModel",
        "vehicleTypedriveModel": "R56Differential",
     }
}

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Parameter name

...

Description

...

Default

...

type

...

Type of vehicle. Currently only Automotive allowed.

...

Automotive

...

vehicleStateEndpoint

...

Endpoint for vehicle state output

...

tcp://*:8999

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canInterface

...

CAN interface used for readin odometry data. Allowed options:

• PeakCAN

• Vector

...

PeakCAN

...

vehicleType

...

Type of vehicle. Currently supported vehicles have to be manually added. Contact us for details.

...

R56 (BMW Mini)

Filter Outputs

The LD filter outputs fusedVehiclePose. 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.

...

   "velError": 0.277777778,
        "omegaError": 0.5,
        "measurementError": 0.1,
        "smoothFit": true,
        "useImuTurnRate": true,
        "imuTurnRateAxis": {
            "x": 1,
            "y": 0,
            "z": 0
        },
        "imuToCarRotation": {
            "w": 1,
            "x": 0,
            "y": 0,
            "z": 0
        }
    }
}

See below a description of the parameter options of the LD filter.

Output Format

See a technical description of FusionHub’s communication interface in one of the following chapters.

JSON

{
    "fusedVehiclePose": {
        "acceleration": {
            "x": 0.0,
            "y": 0.0,
            "z": 0.0
        },
        "globalPosition": {
            "x": 0.0,
            "y": 0.0
        },
        "lastDataTime": {
            "timestamp": 0
        },
        "position": {
            "x": 0,
            "y": 0
        },
        "timestamp": {
            "timestamp": 0
        },
        "utmZone": "31T",
        "yaw": 0
      }
}

Protobuf

syntax = "proto3";

package Fusion.proto;

message Vector2 {
    double x = 2;
    double y = 3;
}

message Vector {
    double x = 2;
    double y = 3;
    double z = 4;
}

message FusedVehiclePose {
    int64 timestamp = 1;
    Vector2 position = 2;
    Vector2 global_position = 3;
    double yaw = 4;
    string utm_zone = 5;
    int64 timecode = 6; // Optional: if 0 not set.    
    Vector acceleration = 7;
}

message StreamData {
    int32 sequence_number = 1;
    FusedVehiclePose fused_vehicle_pose = 9;
}

...

{
    "fusedVehiclePose": {
        "acceleration": {
            "x": 0.0,
            "y": 0.0,
            "z": 0.0
        },
        "globalPosition": {
            "x": 0.0,
            "y": 0.0
        },
        "lastDataTime": {
            "timestamp": 0
        },
        "position": {
            "x": 0,
            "y": 0
        },
        "timestamp": {
            "timestamp": 0
        },
        "utmZone": "31T",
        "yaw": 0
      }
}

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Parameter name

...

Description

...

Unit

...

acceleration

...

3D acceleration vector as measured by IMU. Describes the orientation of the vehicle in the vehicle coordinate system.

...

m/s^2

...

globalPosition

...

Longitude and latitude in degrees

...

degrees

...

lastDataTime

...

Unused

...

s

...

position

...

Position relative to starting point with X pointing North and Y pointing East in the current UTM frame

...

m

...

timestamp

...

Timestamp of data acquisition

...

ns

...

utmZone

...

UTM zone

...

UTM string

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yaw

...

Globally referenced yaw angle

...

rad

High-Dynamics Filter (IMU + GPS)

Node name: gnssImuFusion

Configuration block example (in sinks section)

"gnssImuFusion": {
    "echoFusedPose": false,
    "endpoint": "tcp://*:8803",
    "fuser": {
        "fitModel": "ModelGnssImu",
        "accelError": 0.01,
        "omegaError": 0.02,
        "measurementError": 0.05,
        "imuToCarRotation": {
            "w": 1,
            "x": 0,
            "y": 0,
            "z": 0
        }
    }
}

...

Parameter name

...

Description

...

Default

...

echoFusedPose

...

fusedVehiclePose output is printed to command line

...

false

...

endpoint

...

Output port for the fusion result

...

8801

...

fitModel

...

Model to use for fusion.

...

ModelGnssImu

...

accelError

...

Acceleration error for Kalman filter. Keep default value.

...

0.01

...

omegaError

...

Omega error for Kalman filter. Keep default value.

...

0.02

...

measurementError

...

Measurement error for Kalman filter. Keep default value.

...

0.05

...

imuToCarRotation

...

Orientation quaternion of IMU relative to car frame

...

1, 0, 0, 0

...

smoothFit

...

Enable this option to prevent filter output from jumping between IMU data and GPS measurement. Keep enabled.

...

true

Setting up the ImuToCarRotation parameter

The used car frame is VW coordinate frame,

VW frame
x: back
y: right
z: up

...

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,

IMU mounting
x: forward
y: left
z: up

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

[ -1,  0,  0;
   0, -1,  0;
   0,  0,  1 ] 

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

High-Dynamics Filter

General

The high dynamics filter combines IMU and GPS data to calculate the global position of a vehicle. Instead of using the odometry it uses IMU data to determine the orientation changes of a car on the X, Y and Z axis. The direction of the vehicle is globally referenced from the GPS system. For increasing the direction reference quality a dual-antenna GPS system can be used.

The high dynamics filter works well for scenarios with agressive driving maneuvers such as drifting and cornering. During such maneuvers the turning motion of the wheels generally doesn’t directly correspond with the direction of the vehicle. Therefore for this filter don’t rely on wheel velocity measurements. This filter uses information from the wheels only to determine if the car has come to a full stop.

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. In it’s current state it therefore only works outdoors. In a future version that combines LD and HD filters, this issue will be resolved.

Notes on Filter Output

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

IMU Arrangement

The used car frame is the Volkswagen (VW) coordinate frame convention:

Code Block VW frame x: back y: right z: up

Image Added

The IMU sensor can be mounted in any way but the ImuToCarRotation quaternion needs to be provided to transform the IMU data into VW frame. For example, if the IMU is mounted like follows:

IMU mounting
x: forward
y: left
z: up

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

[ -1,  0,  0;
   0, -1,  0;
   0,  0,  1 ] 

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

"imuToCarRotation": {
    "w": 0,
    "x": 0,
    "y": 0,
    "z": 1
}  

Check this page for more information on how to calculate the orientation quaternion.

Configuration Block

Insert the following configuration block into your config.json file to activate the HD filter. The filter's node name is gnssImuFusion.

"gnssImuFusion": {
    "echoFusedPose": false,
    "endpoint": "tcp://*:8803",
    "fuser": {
        "fitModel": "ModelGnssImu",
        "accelError": 0.01,
        "omegaError": 0.02,
        "measurementError": 0.05,
        "imuToCarRotation": {
            "w": 1,
            "x": 0,
            "y": 0,
            "z": 0
        }
    }
}

See below a description of the parameter options for the HD filter.

Setting up the ImuToCarRotation parameter

This filter needs as input:

...

"vehicle": {
    "type": "Automotive",
    "vehicleStateEndpoint": "tcp://*:8999",
    "settings": {
        "canInterface": "PeakCAN",
        "vehicleType": "R56"
    }
}

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

...

Output data format

FusedVehiclePose

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