factorPoseSE2AndPointXY

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

Since R2022b

Description

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.

Creation

Syntax

F = factorPoseSE2AndPointXY(nodeID)

F = factorPoseSE2AndPointXY(___,Name=Value)

Description

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].

Properties

NodeID — Node ID numbers

N-by-2 matrix of nonnegative integers

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.

Measurement — Measured relative position

zeros(N,2) (default) | N-by-2 matrix

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 — Information matrix associated with uncertainty of measurements

eye(2) (default) | 2-by-2 matrix | 2-by-2-by-N array

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:

$$\left[\begin{array}{cc}\sigma (x,x)& \sigma (x,y)\\ \sigma (y,x)& \sigma (y,y)\end{array}\right]$$

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

Object Functions

nodeType

Get node type of node in factor graph

Examples

Estimate Position Using Landmark Factors

Create a matrix of positions of the landmarks to use for localization, and the real positions of the robot to compare your factor graph estimate against. Use the exampleHelperPlotPositionsAndLandmarks helper function to visualize the landmark points and the real path of the robot..

landmarks = [0 -3  0;
             3  4  0;
             7  1  0];
realpos = [0  0  0;
           2 -2  0;
           5  3  0;
           10 2  0];
exampleHelperPlotPositionsAndLandmarks(realpos,landmarks)

Create Robot Pose Nodes

Create a factor graph, and add a prior factor to loosely fix the start pose of the robot by providing an estimate pose.

fg = factorGraph;
rng(1)
pf = factorPoseSE3Prior(0);

Generate node IDs to use to create three factorTwoPoseSE3 relative pose factors that relate four robot poses. To simulate sensor readings for the measurements of each factor, take the difference between a consecutive pair of ground truth positions, add noise, and append a quaternion of zero to provide a rotation of zero. Then add the prior factor and the pose factors to the factor graph.

zeroQuat = [1 0 0 0];
rpfIDs = generateNodeID(fg,3,"factorTwoPoseSE3")

rpfIDs = 3×2

     0     1
     1     2
     2     3

rpfmeasure = [(diff(realpos) + 0.1*rand(3)) repmat(zeroQuat,3,1)];
rpf = factorTwoPoseSE3(rpfIDs,Measurement=rpfmeasure);
addFactor(fg,pf);
addFactor(fg,rpf);

Create Landmark Factors

Generate node IDs to create three factorPoseSE3AndXYZ landmark factor objects that relate to the pose nodes. The first and second pose nodes observe the first landmark point so they should connect to that landmark with a factor. The second and third pose nodes observe the second landmark. The third and fourth pose nodes observe the third landmark.

landmarkIDs = generateNodeID(fg,3)'

landmarkIDs = 3×1

     4
     5
     6

The landmark factors used here are for 3-D state space but the process is identical for landmark factors for 2-D state space. Add some random number to the relative position between the landmark and the ground truth position to simulate real sensor measurements. Then create the landmark factors and add them to the factor graph.

lmf1measure = [landmarks(1,:) - realpos(1:2,:)] + 0.5*rand(1,3);
lmf2measure = [landmarks(2,:) - realpos(2:3,:)] + 0.5*rand(1,3);
lmf3measure = [landmarks(3,:) - realpos(3:4,:)] + 0.5*rand(1,3);
lmf1 = factorPoseSE3AndPointXYZ([[0 1]' repmat(landmarkIDs(1),2,1)],Measurement=lmf1measure);
lmf2 = factorPoseSE3AndPointXYZ([[1 2]' repmat(landmarkIDs(2),2,1)],Measurement=lmf2measure);
lmf3 = factorPoseSE3AndPointXYZ([[2 3]' repmat(landmarkIDs(3),2,1)],Measurement=lmf3measure);
addFactor(fg,lmf1);
addFactor(fg,lmf2);
addFactor(fg,lmf3);

Optimize Factor Graph

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

fgso = factorGraphSolverOptions;
optimize(fg,fgso)

ans = struct with fields:
             InitialCost: 72.6129
               FinalCost: 0.0011
      NumSuccessfulSteps: 4
    NumUnsuccessfulSteps: 0
               TotalTime: 4.2391e-04
         TerminationType: 0
        IsSolutionUsable: 1

Visualize and Compare Results

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

poseIDs = nodeIDs(fg,NodeType="POSE_SE3");
fgposopt = nodeState(fg,poseIDs)

fgposopt = 4×7

    0.0000    0.0000    0.0000    1.0000    0.0000   -0.0000    0.0000
    2.0278   -1.9778    0.0173    1.0000    0.0018   -0.0034    0.0014
    5.0684    3.0500    0.0871    0.9999   -0.0010   -0.0072    0.0089
   10.0844    2.1475    0.1972    0.9999    0.0006   -0.0121    0.0100

fglmopt = nodeState(fg,landmarkIDs);
exampleHelperPlotPositionsAndLandmarks(realpos,landmarks,fgposopt,fglmopt)

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2022b

R2023a: Specify multiple factors

The NodeID, Measurement, and Information properties now accept additional rows to specify multiple factors.