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dynamicEvidentialGridMap

Dynamic grid map output from trackerGridRFS

Description

The dynamicEvidentialGridMap object represents the dynamic map estimate obtained from the grid-based tracker, trackerGridRFS. You can visualize the dynamic map and obtain the estimated values by using the object functions of dynamicEvidentialGridMap. The dynamicEvidentialGridMap object is a handle object.

Creation

You can generate a dynamicEvidentialGridMap object using a trackerGridRFS object. See the Usage syntax of trackerGridRFS and the Obtain Estimated Values at Grid Level using dynamicEvidentialGridMap example for details.

Properties

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This property is read-only.

Motion model for tracking, specified as 'constant-velocity', 'constant-acceleration', or 'constant-turn-rate'. The particle state and object state for each motion model are:

MotionModelParticle StateObject State
'constant-velocity'[x; vx; y; vy] [x; vx; y; vy; yaw; L; W]
'constant-acceleration'[x; vx; ax; y; vy; ay][x; vx; ax; y; vy; ay; yaw; L; W]
'constant-turn-rate'[x; vx; y; vy; w][x; vx; y; vy; w; yaw; L; W]

where:

  • x — Position of the object in the x direction of the tracking frame (m)

  • y — Position of the object in the y direction of the tracking frame (m)

  • vx — Velocity of the object in the x direction of the tracking frame (m/s)

  • vy — Velocity of the object in the y direction of the tracking frame (m/s)

  • ax — Acceleration of the object in the x direction of the tracking frame (m/s2)

  • ay — Acceleration of the object in the y direction of the tracking frame (m/s2)

  • w — Yaw-rate of the object in the tracking frame (degree/s)

  • yaw — Yaw angle of the object in the tracking frame (deg)

  • L — Length of the object (m)

  • W — Width of the object (m)

This property is read-only.

Number of state variables corresponding to the motion model specified in the MotionModel property, specified as a positive integer.

Example: 6

This property is read-only.

x-direction dimension of the grid in the local coordinates, specified as a positive scalar in meters.

This property is read-only.

y-direction dimension of the grid in the local coordinates, specified as a positive scalar in meters.

This property is read-only.

Resolution of the grid, specified as a positive scalar. GridResolution represents the number of cells per meter of the grid for both the x- and y-direction of the grid.

This property is read-only.

Location of the grid origin in the local coordinate frame, specified as a two-element real-valued vector in meters. The grid origin represents the bottom-left corner of the grid map.

Object Functions

getEvidencesGet estimated occupancy and free evidences
getOccupancyGet estimated occupancy probabilities
getStateGet full estimated state and associated uncertainty
getVelocityGet estimated velocity and associated uncertainty
showVisualize dynamic evidential grid map

Examples

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Create a tracking scenario.

rng(2021);% For reproducible results
scene = trackingScenario('UpdateRate',5,'StopTime',5);

Add a platform. Mount a lidar sensor on the platform.

plat = platform(scene);
lidar = monostaticLidarSensor(1,'DetectionCoordinates','Body');

Add two targets and define their position, velocity, orientation, dimension, and meshes.

for i = 1:2
    target = platform(scene);
    x = 50*(2*rand-1);
    y = 50*(2*rand-1);
    vx = 5*(2*rand-1);
    vy = 5*(2*rand-1);
    target.Trajectory.Position = [x y 0];
    target.Trajectory.Velocity = [vx vy 0];
    target.Trajectory.Orientation = quaternion([atan2d(vy,vx),0,0],'eulerd','ZYX','frame');
    target.Mesh = extendedObjectMesh('sphere');
    target.Dimensions = struct('Length',4, ...
        'Width',4, ...
        'Height',2, ...
        'OriginOffset',[0 0 0]);
end

Define the configuration of the sensor.

config = trackingSensorConfiguration(1, ...
    'SensorLimits',[-180 180;0 100], ...
    'SensorTransformParameters',struct, ...
    'IsValidTime',true);

Create a grid-based tracker.

tracker = trackerGridRFS('SensorConfigurations',config, ...
    'AssignmentThreshold',5, ...
    'MinNumCellsPerCluster',4, ...
    'ClusteringThreshold',3);

Advance scenario and run the tracker based on the lidar data.

while advance(scene)
    % Current time
    time = scene.SimulationTime;

    % Generate point cloud
    tgtMeshes = targetMeshes(plat);
    [ptCloud, config] = lidar(tgtMeshes, time);

    % Format the data for the tracker
    sensorData = struct('Time',time, ...
        'SensorIndex',1, ...
        'Measurement',ptCloud', ...
        'MeasurementParameters',struct ...
        );

    % Call tracker using sensorData to obtain the map in addition
    % to tracks
    [tracks, ~, ~, map] = tracker(sensorData,time);

    % Obtain the estimated occupancy probability of each cell
    P_occ = getOccupancy(map);

    % Obtain the estimated evidences for each cell
    [m_occ, m_free] = getEvidences(map);

    % Obtain the estimated velocity for each cell
    [v, Pv] = getVelocity(map);

    % Obtain the estimated state for each cell
    [x, P] = getState(map);
    
    % Show the map
    show(map,'InvertColors',true)
end

Figure contains an axes object. The axes object contains 2 objects of type image, surface.

Extended Capabilities

Introduced in R2021a