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Added comments to be explicit about b = z - h(x_bar)

release/4.3a0
dellaert 2015-03-05 16:59:10 -08:00
parent 5cdb8ddb76
commit 6212034976
1 changed files with 16 additions and 11 deletions
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27
gtsam/nonlinear/ExpressionFactor.h
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@ -66,20 +66,23 @@ public:
virtual Vector unwhitenedError(const Values& x,
boost::optional<std::vector<Matrix>&> H = boost::none) const {
if (H) {
const T value = expression_.value(x, keys_, dims_, *H);
return traits<T>::Local(measurement_, value);
const T hx = expression_.value(x, keys_, dims_, *H); // h(x)
return traits<T>::Local(measurement_, hx); // h(x) - z
} else {
const T value = expression_.value(x);
return traits<T>::Local(measurement_, value);
const T hx = expression_.value(x); // h(x)
return traits<T>::Local(measurement_, hx); // h(x) - z
}
}
virtual boost::shared_ptr<GaussianFactor> linearize(const Values& x) const {
/**
* Linearize the factor into a JacobianFactor
*/
virtual boost::shared_ptr<GaussianFactor> linearize(const Values& x_bar) const {
// Only linearize if the factor is active
if (!active(x))
if (!active(x_bar))
return boost::shared_ptr<JacobianFactor>();
// Create a writeable JacobianFactor in advance
// Create a writable JacobianFactor in advance
// In case noise model is constrained, we need to provide a noise model
bool constrained = noiseModel_->isConstrained();
boost::shared_ptr<JacobianFactor> factor(
@ -88,17 +91,19 @@ public:
new JacobianFactor(keys_, dims_, Dim));
// Wrap keys and VerticalBlockMatrix into structure passed to expression_
VerticalBlockMatrix& Ab = factor->matrixObject();
VerticalBlockMatrix& Ab = factor->matrixObject(); // reference, no malloc !
JacobianMap jacobianMap(keys_, Ab);
// Zero out Jacobian so we can simply add to it
Ab.matrix().setZero();
// Get value and Jacobians, writing directly into JacobianFactor
T value = expression_.value(x, jacobianMap); // <<< Reverse AD happens here !
T hx = expression_.value(x_bar, jacobianMap); // <<< Reverse AD happens here !
// Evaluate error and set RHS vector b
Ab(size()).col(0) = -traits<T>::Local(measurement_, value);
// Evaluate error and set RHS vector b = - (h(x_bar) - z) = z-h(x_bar)
// Indeed, nonlinear error |h(x_bar+dx)-z| ~ |h(x_bar) + A*dx - z|
// = |A*dx - (z-h(x_bar))|
Ab(size()).col(0) = - traits<T>::Local(measurement_, hx); // - unwhitenedError(x_bar)
// Whiten the corresponding system, Ab already contains RHS
Vector dummy(Dim);