# factorGraph

Bipartite graph of factors and nodes

Since R2022a

## Description

A `factorGraph` object stores a bipartite graph consisting of factors connected to variable nodes. The nodes represent the unknown random variables in an estimation problem, such as robot poses, and the factors represent probabilistic constraints on those nodes, derived from measurements or prior knowledge. During optimization, the factor graph uses all the factors and current node states to update the node states.

To use the factor graph:

1. Create an empty `factorGraph` object.

2. For each desired factor type:

1. Generate node IDs using the `generateNodeID` object function.

2. Define factors with the desired node IDs, using any of the supported factor objects:

3. Add factors to the factor graph using the `addFactor` object function. If the factor graph does not contain a node with the specified ID, the function automatically creates a node with that ID and adds it to the factor graph when adding the factor to the factor graph. If the factor graph contains a node with the specified ID, ensure that adding the new factor does not cause a node type mismatch. For more information, see Tips. For a list of expected node types for each factor, see Expected Node Types of Factor Objects.

3. Check if all the nodes in the factor graph are connected to at least one other node using the `isConnected` object function.

4. Create a `factorGraphSolverOptions` object to specify factor graph solver options.

5. Optimize the factor graph using the `optimize` object function with the desired factor graph solver options.

6. Extract factor graph node data, such as node IDs and node states, using the `nodeIDs` and `nodeState` object functions.

## Creation

### Syntax

``fg = factorGraph``

### Description

example

````fg = factorGraph` creates an empty `factorGraph` object.```

## Properties

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Number of nodes in the factor graph, specified as a positive integer. `NumNodes` has a value of `0` when the factor graph is empty and `NumNodes` increases each time you add a factor that specifies new node IDs to the factor graph.

The nodes in the factor graph can be any of these types:

• `"POSE_SE2"` — Pose in SE(2) state space

• `"POSE_SE3"` — Pose in SE(3) state space

• `"VEL3"` — 3-D velocity

• `"POINT_XY"` — 2-D point

• `"POINT_XYZ"` — 3-D point

• `"IMU_BIAS"` — IMU gyroscope and accelerometer bias

To check the node type of a node in the graph, use the `nodeType` function.

Note

The factor graph sets the node type when you add the factor object that specifies that node to the factor graph. You cannot change the node type of a node after you add it to the graph.

Number of factors in the factor graph, specified as a positive integer. `NumFactors` has a value of `0` when the factor graph is empty and `NumFactors` increases each time you add a factor to the factor graph.

You can use `addfactor` to add any of these factor objects to the factor graph:

Relate Poses to Sensor Measurements

• `factorGPS` — Connect SE(3) pose node (`"POSE_SE3"`) to a GPS measurement.

• `factorIMU` — Connect two SE(3) pose nodes (`"POSE_SE3"`), two 3-D velocity nodes (`"VEL3"`), and two IMU bias nodes (`"IMU_BIAS"`) using an IMU measurement.

Relate Poses to Landmark Positions

• `factorCameraSE3AndPointXYZ` — Connect the SE(3) pose node of a pinhole camera (`"POSE_SE3"`) to 3-D landmark nodes (`"Point_XYZ"`) using relative pose measurements.

• `factorPoseSE2AndPointXY` — Connect a SE(2) pose node (`"POSE_SE2"`) to 2-D landmark nodes (`"Point_XY"`) using relative pose measurements.

• `factorPoseSE3AndPointXYZ` — Connect a SE(3) pose node (`"POSE_SE3"`) to 3-D landmark nodes (`"Point_XYZ"`) using relative pose measurements.

Relate Poses to Each Other

• `factorTwoPoseSE2` — Connect pairs of SE(2) pose nodes (`"POSE_SE2"`) with relative poses using relative pose measurements.

• `factorTwoPoseSE3` — Connect pairs of SE(3) pose nodes (`"POSE_SE3"`) with relative poses using relative pose measurements.

Relate Poses or Velocities to Prior-Known Measurements

• `factorIMUBiasPrior` — Connect SE(3) pose nodes (`"POSE_SE3"`), 3-D velocity nodes (`"VEL3"`), and IMU bias nodes (`"IMU_BIAS"`) to prior-known IMU measurements.

• `factorPoseSE3Prior` — Connect SE(3) pose nodes (`"POSE_SE3"`) to prior-known SE(3) pose measurements.

• `factorVelocity3Prior` — Connect 3-D velocity node (`"VEL_3"`) to prior-known SE(3) velocity measurements.

## Object Functions

 `addFactor` Add factor to factor graph `fixNode` Fix or free nodes in factor graph `generateNodeID` Generate new node IDs `hasNode` Check if node ID exists in factor graph `isConnected` Check if factor graph is connected `isNodeFixed` Check if node is fixed `nodeIDs` Get node IDs in factor graph `nodeState` Get or set node state in factor graph `nodeType` Get node type of node in factor graph `optimize` Optimize factor graph `removeFactor` Remove factor from factor graph `removeNode` Remove node from factor graph `show` Plot pose nodes, pose node edges, and landmark nodes of factor graph

## Examples

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

```gndtruth = [0 0 0; 2 0 pi/2; 2 2 pi; 0 2 pi]; landmarks = [3 0; 0 3]; exampleHelperPlotGroundTruth(gndtruth,landmarks)```

Use the `exampleHelperSimpleFourPoseGraph` helper function to create a factor graph contains four poses related by three 2-D two-pose factors. For more details, see the `factorTwoPoseSE2` object page.

`fg = exampleHelperSimpleFourPoseGraph(gndtruth);`

Create Landmark Factors

Generate node IDs to create two node IDs for two landmarks. The second and third pose nodes observe the first landmark point so they should connect to that landmark with a factor. The third and fourth pose nodes observe the second landmark.

```lmIDs = generateNodeID(fg,2); lmFIDs = [1 lmIDs(1); % Pose Node 1 <-> Landmark 1 2 lmIDs(1); % Pose Node 2 <-> Landmark 1 2 lmIDs(2); % Pose Node 2 <-> Landmark 2 3 lmIDs(2)]; % Pose Node 3 <-> Landmark 2```

Define the relative position measurements between the position of the poses and their landmarks in the reference frame of the pose node. Then add some noise.

```lmFMeasure = [0 -1; % Landmark 1 in pose node 1 reference frame -1 2; % Landmark 1 in pose node 2 reference frame 2 -1; % Landmark 2 in pose node 2 reference frame 0 -1]; % Landmark 2 in pose node 3 reference frame lmFMeasure = lmFMeasure + 0.1*rand(4,2);```

Create the landmark factors with those relative measurements and add it to the factor graph.

```lmFactor = factorPoseSE2AndPointXY(lmFIDs,Measurement=lmFMeasure); addFactor(fg,lmFactor);```

Set the initial state of the landmark nodes to the real position of the landmarks with some noise.

`nodeState(fg,lmIDs,landmarks+0.1*rand(2));`

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.

```rng default optimize(fg)```
```ans = struct with fields: InitialCost: 0.0538 FinalCost: 6.2053e-04 NumSuccessfulSteps: 4 NumUnsuccessfulSteps: 0 TotalTime: 1.6499e-04 TerminationType: 0 IsSolutionUsable: 1 OptimizedNodeIDs: [1 2 3 4 5] FixedNodeIDs: 0 ```

Visualize and Compare Results

Get and store the updated node states for the robot and landmarks. Then 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_SE2")`
```poseIDs = 1×4 0 1 2 3 ```
`poseStatesOpt = nodeState(fg,poseIDs)`
```poseStatesOpt = 4×3 0 0 0 2.0815 0.0913 1.5986 1.9509 2.1910 -3.0651 -0.0457 2.0354 -2.9792 ```
`landmarkStatesOpt = nodeState(fg,lmIDs)`
```landmarkStatesOpt = 2×2 3.0031 0.1844 -0.1893 2.9547 ```
```handle = show(fg,Orientation="on",OrientationFrameSize=0.5,Legend="on"); grid on; hold on; exampleHelperPlotGroundTruth(gndtruth,landmarks,handle);```

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## Tips

• To specify multiple factors and nodes at once for a specific factor type, use the `generateNodeID` function and specify the number of factors and the factor type. See the `generateNodeID` function for more details.

```poseIDPairs = generateNodeID(fg,3,"factorTwoPoseSE2"); ftpse2 = factorTwoPoseSE2(poseIDPairs);```
• You can get the states of all the pose nodes by first using the `nodeIDs` function and specifying the node type as `"POSE_SE2"` for SE(2) robot poses and `"POSE_SE3"` for SE(3) robot poses. Then, use the `nodeState` function with those node IDs to get the node states of the robot pose nodes.

```poseIDs = nodeIDs(fg,NodeType="POSE_SE2"); poseStates = nodeState(fg,poseIDs);```
• Check the types of nodes that each factor creates or connects to before adding factors to the factor graph to avoid node type mismatch errors. For a list of expected node types for each factor, see Expected Node Types of Factor Objects.

## References

[1] Dellaert, Frank. Factor graphs and GTSAM: A Hands-On Introduction. Georgia: Georgia Tech, September, 2012.

## Version History

Introduced in R2022a