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The low dynamics (LD) filter combines odometry, GPS and IMU data to calculate the global position of a vehicle. This filter was initially concipated wokring 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.

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Filter Inputs

This filter needs the following sources as input. One option is to operate the filter with our with our LPMS-IG1P sensor that contains IMU and a GPS receiver. This allows for stadard 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": {
    "type": "DualRtk",
    "settings": {
        "sensor1": {
            // If specification needed, insert first IG1 sensor name here
            //"name": "ig1p232800650050",
            "autodetectType": "ig1p"
        },
        "rtcm": true,
        "imuEndpoint": "tcp://*:8802"
    }
}

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Option 2 - Separate LPMS-IG1 IMU and RTK GPS sources (with NTRIP

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caster for RTK correction)

LPMS-IG1 Source

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

"RTCM": {
    "type": "NTRIP",
    "settings": {
        "host": "some-host-name",
        "port": "2101",
        "mountpoint": "some-mount-point",
   

RTCM Source

"RTCM": {
    "usertype": "some-userNTRIP",
  
     "passwordsettings": "some-password",{
        "userAgenthost": "LPVRsome-host-name",
        "initialLatitudeport": 35.65736"2101",
        "initialLongitudemountpoint": 139.73239"some-mount-point",
        "forwardGnssuser": "some-user",
true     } },  "gnsspassword": {
"some-password",
   "type": "NMEA",     "settingsuserAgent": {"LPVR",
        "portinitialLatitude": "/dev/ttyUSB0"35.65736,
        "baudrateinitialLongitude": 115200139.73239,
        "rtcmforwardGnss":  true
    }
},

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GNSS Source

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Parameter name

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Description

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Default

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type

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

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NMEA

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port

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Serial port number for gnss source.

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baudrate

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Serial port baudrate to connect to gnss source.

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rtcm

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

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false

• CAN bus and vehicle decoder source

"gnss": {
    "type": "Automotive",
    "vehicleStateEndpoint": "tcp://*:8999NMEA",
    "settings": {
        "canInterfaceport": "PeakCAN/dev/ttyUSB0",
        "vehicleTypebaudrate": 115200,
        "R56rtcm": true
    }
}

This Filter outputs:

• fusedVehiclePose

Output Data Format

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CAN bus and vehicle decoder source

"vehicle": {
    "fusedVehiclePosetype": "Automotive",
{    "vehicleStateEndpoint": "tcp://*:8999",
    "accelerationsettings": {
  
         "xcanInterface": 0.0,
  "PeakCAN",
         "yvehicleType": 0.0,"R56"
            "z": 0.0
        },
        "globalPosition": {
            "x": 0.0,
            "y": 0.0
        },
        "lastDataTime": {
            "timestamp": 0
        },
        "position": {
            "x": 0,
            "y": 0
        },
        "timestamp": {
            "timestamp": 0
        },
        "utmZone": "31T",}
}

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.

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. The exact output data format is described below.

{
    "fusedVehiclePose": {
        "yawacceleration": {
0       }
}

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Parameter name

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Description

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Unit

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acceleration

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

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m/s^2

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globalPosition

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Longitude and latitude in degrees

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degrees

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lastDataTime

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Unused

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s

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position

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

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m

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timestamp

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Timestamp of data acquisition

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ns

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utmZone

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UTM zone

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UTM string

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yaw

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Globally referenced yaw angle

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rad

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.

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     "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
      }
}

High-Dynamics Filter (IMU + GPS)

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Information regarding the ENU coordinate system is here: https://en.wikipedia.org/wiki/Local_tangent_plane_coordinates

Example Configuration

Playback and fusion of prerecorded data: gpsImuFusionPlayback.json

Data Playback [DEPRECATED, switch over to ReplayExecutable]

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ReplayExecutable.exe -r <path/to/file.json> [--replay-speed 1] [--queue-size 100] [--echo-data] [--verbose]

A normal FusionHub program can then receive the file data by having an endpoints source defined in the configuration file:

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