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Factor relating SE(2) position and 2-D point

Since R2022b


    The factorPoseSE2AndPointXY object contains factors that each describe the relationship between a position in the SE(2) state space and a 2-D landmark point. You can use this object to add one or more factors to a factorGraph object.



    F = factorPoseSE2AndPointXY(nodeID) creates a factorPoseSE2AndPointXY object, F, with the node identification numbers property NodeID set to nodeID.


    F = factorPoseSE2AndPointXY(___,Name=Value) specifies properties using one or more name-value arguments in addition to the argument from the previous syntax. For example, factorPoseSE2AndPointXY([1 2],Measurement=[1 5]) sets the Measurement property of the factorPoseSE2AndPointXY object to [1 5].


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    This property is read-only.

    Node ID numbers, specified as an N-by-2 matrix of nonnegative integers, where N is the total number of desired factors. Each row represents a factor connecting a node of type, POSE_SE2 to a node of type POINT_XY in the form [PoseID PointID], where PoseID is the ID of the POSE_SE2 node and PointID is the ID of the POINT_XY node in the factor graph.

    If a factor in the factorPoseSE2AndPointXY object specifies an ID that does not correspond to a node in the factor graph, the factor graph automatically creates a node of the required type with that ID and adds it to the factor graph when adding the factor to the factor graph.

    You must specify this property at object creation.

    For more information about the expected node types of all supported factors, see Expected Node Types of Factor Objects.

    Measured relative position between the current position and landmark point, specified as an N-by-2 matrix where each row is of the form [dx dy], in meters. N is the total number of factors, and dx and dy are the change in position in x and y, respectively.

    Information matrix associated with the uncertainty of the measurements, specified as a 2-by-2 matrix or a 2-by-2-by-N array. N is the total number of factors specified by the factorPoseSE2AndPointXY object. Each information matrix corresponds to the measurements of the corresponding node in NodeID.

    If you specify this property as a 2-by-2 matrix when NodeID contains more than one row, the information matrix corresponds to all measurements in Measurement.

    This information matrix is the inverse of the covariance matrix, where the covariance matrix is of the form:


    Each element indicates the covariance between two variables. For example, σ(x,y) is the covariance between x and y.

    Object Functions

    nodeTypeGet node type of node in factor graph


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    Define the ground truth for five robot poses as a loop and create a factor graph.

    gndtruth = [0 0 0; 
                2 0 pi/2; 
                2 2 pi; 
                0 2 3*pi
                0 0 0];
    fg = factorGraph;

    Generate the node IDs needed to create three factorTwoPoseSE2 factors and then manually add the Because node 4 would coincide directly on top of the node 0, instead of specifying a factor that connects node 3 to a new node 4, create a loop closure by adding another factor that relates node 3 to node 0.

    poseFIDs = generateNodeID(fg,3,"factorTwoPoseSE2");
    poseFIDs = [poseFIDs; 3 0]
    poseFIDs = 4×2
         0     1
         1     2
         2     3
         3     0

    Define the relative measurement between each consecutive pose and add a little noise so the measurement is more like a sensor reading.

    relMeasure = [2 0 pi/2; 2 0 pi/2; 2 0 pi/2; 2 0 pi/2] + 0.1*rand(4,3);

    Create the factorTwoPoseSE2 factors with the defined relative measurements and then add the factors to the factor graph.

    poseFactor = factorTwoPoseSE2(poseFIDs,Measurement=relMeasure);

    Get the node IDs of all of the SE2 pose nodes in the factor graph.

    poseIDs = nodeIDs(fg,NodeType="POSE_SE2");

    Because the POSE_SE2 type nodes have a default state of [0 0 0], you should provide an initial guess for the state. Normally this is from an odometry sensor on the robot. But for this example, use the ground truth with some noise.

    predictedState = gndtruth(1:4,:);
    predictedState(2:4,:) = predictedState(2:4,:) + 0.1*rand(3,3);

    Then set the states of the pose nodes to the predicted guess states.


    Fix the first pose node. Because the nodes are all relative to each other, they need a known state to be an anchor.


    Optimize Factor Graph and Visual Results

    Optimize the factor graph with the default solver options. The optimization updates the states of all nodes in the factor graph so the poses of vehicle update.

    rng default
    ans = struct with fields:
                 InitialCost: 6.1614
                   FinalCost: 0.0118
          NumSuccessfulSteps: 5
        NumUnsuccessfulSteps: 0
                   TotalTime: 0.0081
             TerminationType: 0
            IsSolutionUsable: 1
            OptimizedNodeIDs: [1 2 3]
                FixedNodeIDs: 0

    Get and store the updated node states for the robot. Then plot the results, comparing the factor graph estimate of the robot path to the known ground truth of the robot.

    poseStatesOpt = nodeState(fg,poseIDs)
    poseStatesOpt = 4×3
             0         0         0
        2.0777    0.0689    1.5881
        2.0280    2.1646   -3.1137
        0.0132    2.0864   -1.6014
    hold on
    plot([poseStatesOpt(:,1); 0],[poseStatesOpt(:,2); 0],Marker="*",LineStyle="--",LineWidth=1);
    legend(["Ground Truth","Opt. Position"]);
    s2 = se2(poseStatesOpt,"xytheta");
    axis padded
    hold off

    Figure contains an axes object. The axes object contains 22 objects of type patch, line, text. These objects represent Ground Truth, Opt. Position.

    Note that the poses do not match perfectly with the ground truth because there are not many factors in this graph that the optimize function can use to provide a more accurate solution. The accuracy can be improved by using more accurate measurements, accurate initial state guesses, and adding additional factors to add more information for the optimizer to use.

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    Version History

    Introduced in R2022b

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