Main Content

factorGraph

Bipartite graph of factors and nodes

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, and the factors represent probabilistic constraints on those variables, derived from measurements or prior knowledge. Add factors and nodes to the factor graph by using the addFactor function.

Creation

Description

example

G = factorGraph creates an empty factorGraph object.

Properties

expand all

This property is read-only.

Number of nodes in the factor graph, specified as a positive integer.

This property is read-only.

Number of factors in the factor graph, specified as a positive integer.

Object Functions

addFactorAdd factor to factor graph
fixNodeFix or free node in factor graph
hasNodeCheck if node ID exists in factor graph
isConnectedCheck if factor graph is connected
isNodeFixedCheck if node is fixed
nodeIDsGet all node IDs in factor graph
nodeStateGet or set node state in factor graph
nodeTypeGet node type of node in factor graph
optimizeOptimize factor graph

Examples

collapse all

Create and optimize a factor graph with custom solver options.

Create Factor Graph and Solver Settings

Create a factor graph and solver options with custom settings. Set the maximum number of iterations to 1000 and set the verbosity of the optimize output to 2.

G = factorGraph;
optns = factorGraphSolverOptions(MaxIterations=1000,VerbosityLevel=2)
optns = 
  factorGraphSolverOptions with properties:

              MaxIterations: 1000
          FunctionTolerance: 1.0000e-06
          GradientTolerance: 1.0000e-10
              StepTolerance: 1.0000e-08
             VerbosityLevel: 2
    TrustRegionStrategyType: 1

Add GPS Factor

Create a GPS factor with node identification number of 1 with NED ReferenceFrame and add it to the factor graph.

fgps = factorGPS(1,ReferenceFrame="NED");
addFactor(G,fgps);

Optimize Factor Graph

Optimize the factor graph with the custom settings. The results of the optimization are displayed with the level of detail depending on the VerbosityLevel.

optimize(G,optns);
iter      cost      cost_change  |gradient|   |step|    tr_ratio  tr_radius  ls_iter  iter_time  total_time
   0  0.000000e+00    0.00e+00    0.00e+00   0.00e+00   0.00e+00  1.00e+04        0    2.38e-05    2.05e-04
Terminating: Gradient tolerance reached. Gradient max norm: 0.000000e+00 <= 1.000000e-10

Solver Summary (v 2.0.0-eigen-(3.3.4)-no_lapack-eigensparse-no_openmp)

                                     Original                  Reduced
Parameter blocks                            1                        1
Parameters                                  7                        7
Effective parameters                        6                        6
Residual blocks                             1                        1
Residuals                                   3                        3

Minimizer                        TRUST_REGION

Sparse linear algebra library    EIGEN_SPARSE
Trust region strategy                  DOGLEG (TRADITIONAL)

                                        Given                     Used
Linear solver          SPARSE_NORMAL_CHOLESKY   SPARSE_NORMAL_CHOLESKY
Threads                                     1                        1
Linear solver ordering              AUTOMATIC                        1

Cost:
Initial                          0.000000e+00
Final                            0.000000e+00
Change                           0.000000e+00

Minimizer iterations                        1
Successful steps                            1
Unsuccessful steps                          0

Time (in seconds):
Preprocessor                         0.000181

  Residual only evaluation           0.000000 (0)
  Jacobian & residual evaluation     0.000015 (1)
  Linear solver                      0.000000 (0)
Minimizer                            0.004343

Postprocessor                        0.000021
Total                                0.004546

Termination:                      CONVERGENCE (Gradient tolerance reached. Gradient max norm: 0.000000e+00 <= 1.000000e-10)

References

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

Version History

Introduced in R2022a