Updated Marginals doxygen

gtsam/nonlinear/Marginals.cpp

@@ -39,6 +39,10 @@ Marginals::Marginals(const NonlinearFactorGraph& graph, const Values& solution,
 }
 
 Matrix Marginals::marginalCovariance(Key variable) const {
+  return marginalInformation(variable).inverse();
+}
+
+Matrix Marginals::marginalInformation(Key variable) const {
   // Get linear key
   Index index = ordering_[variable];
 
@@ -50,18 +54,16 @@ Matrix Marginals::marginalCovariance(Key variable) const {
     marginalFactor = bayesTree_.marginalFactor(index, EliminateQR);
 
   // Get information matrix (only store upper-right triangle)
-  Matrix info;
   if(typeid(*marginalFactor) == typeid(JacobianFactor)) {
     JacobianFactor::constABlock A = static_cast<const JacobianFactor&>(*marginalFactor).getA();
-    info = A.transpose() * A; // Compute A'A
+    return A.transpose() * A; // Compute A'A
   } else if(typeid(*marginalFactor) == typeid(HessianFactor)) {
     const HessianFactor& hessian = static_cast<const HessianFactor&>(*marginalFactor);
     const size_t dim = hessian.getDim(hessian.begin());
-    info = hessian.info().topLeftCorner(dim,dim).selfadjointView<Eigen::Upper>(); // Take the non-augmented part of the information matrix
+    return hessian.info().topLeftCorner(dim,dim).selfadjointView<Eigen::Upper>(); // Take the non-augmented part of the information matrix
+  } else {
+    throw runtime_error("Internal error: Marginals::marginalInformation expected either a JacobianFactor or HessianFactor");
   }
-
-  // Compute covariance
-  return info.inverse();
 }
 
 } /* namespace gtsam */

gtsam/nonlinear/Marginals.h

@@ -25,21 +25,33 @@
 namespace gtsam {
 
 /**
- * A class for computing marginals in a NonlinearFactorGraph
+ * A class for computing Gaussian marginals of variables in a NonlinearFactorGraph
  */
 class Marginals {
 
 public:
 
+  /** The linear factorization mode - either CHOLESKY (faster and suitable for most problems) or QR (slower but more numerically stable for poorly-conditioned problems). */
   enum Factorization {
     CHOLESKY,
     QR
   };
 
+  /** Construct a marginals class.
+   * @param graph The factor graph defining the full joint density on all variables.
+   * @param solution The linearization point about which to compute Gaussian marginals (usually the MLE as obtained from a NonlinearOptimizer).
+   * @param factorization The linear decomposition mode - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
+   */
   Marginals(const NonlinearFactorGraph& graph, const Values& solution, Factorization factorization = CHOLESKY);
 
+  /** Compute the marginal covariance of a single variable */
   Matrix marginalCovariance(Key variable) const;
 
+  /** Compute the marginal information matrix of a single variable.  You can
+   * use LLt(const Matrix&) or RtR(const Matrix&) to obtain the square-root information
+   * matrix. */
+  Matrix marginalInformation(Key variable) const;
+
 protected:
 
   GaussianFactorGraph graph_;