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Fixed doxygen warnings.

release/4.3a0
Summit Patel 2012-06-05 19:54:14 +00:00
parent 00b6f177d2
commit 5bcb0de588
6 changed files with 15 additions and 17 deletions
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4
gtsam/inference/ClusterTree.h
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@ -32,8 +32,8 @@ namespace gtsam {
/**
* A cluster-tree is associated with a factor graph and is defined as in Koller-Friedman:
* each node k represents a subset C_k \sub X, and the tree is family preserving, in that
* each factor f_i is associated with a single cluster and scope(f_i) \sub C_k.
* each node k represents a subset \f$ C_k \sub X \f$, and the tree is family preserving, in that
* each factor \f$ f_i \f$ is associated with a single cluster and \f$ scope(f_i) \sub C_k \f$.
* \nosubgrouping
*/
template <class FG>

7
gtsam/linear/NoiseModel.h
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@ -146,7 +146,6 @@ namespace gtsam {
/**
* A Gaussian noise model created by specifying a square root information matrix.
* @param smart, check if can be simplified to derived class
*/
static shared_ptr SqrtInformation(const Matrix& R) {
return shared_ptr(new Gaussian(R.rows(),R));
@ -154,7 +153,7 @@ namespace gtsam {
/**
* A Gaussian noise model created by specifying a covariance matrix.
* @param smart, check if can be simplified to derived class
* @param smart check if can be simplified to derived class
*/
static shared_ptr Covariance(const Matrix& covariance, bool smart=false);
@ -267,7 +266,7 @@ namespace gtsam {
/**
* A diagonal noise model created by specifying a Vector of variances, i.e.
* i.e. the diagonal of the covariance matrix.
* @param smart, check if can be simplified to derived class
* @param smart check if can be simplified to derived class
*/
static shared_ptr Variances(const Vector& variances, bool smart = false);
@ -503,7 +502,7 @@ namespace gtsam {
/**
* An isotropic noise model created by specifying a variance = sigma^2.
* @param smart, check if can be simplified to derived class
* @param smart check if can be simplified to derived class
*/
static shared_ptr Variance(size_t dim, double variance, bool smart = false);

4
gtsam/mainpage.dox
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@ -9,9 +9,9 @@ namespace gtsam {
\section new_factor_variable_types Creating new factor and variable types
GTSAM comes with a set of variable and factor types typically used in SFM and
SLAM. Geometry variables such as points and poses are in the \ref geometry
SLAM. Geometry variables such as points and poses are in the geometry
subdirectory and module. Factors such as BetweenFactor and BearingFactor are in
the \ref gtsam/slam directory.
the gtsam/slam directory.
To use GTSAM to solve your own problems, you will often have to create new factor types, which derive either from NonlinearFactor or NoiseModelFactor, or one of their derived types. Here is an outline of the options:

7
gtsam/nonlinear/NonlinearFactor.h
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@ -228,7 +228,6 @@ public:
/**
* Constructor
* @param keys The variables involved in this factor
*/
template<class ITERATOR>
NoiseModelFactor(const SharedNoiseModel& noiseModel, ITERATOR beginKeys, ITERATOR endKeys)
@ -379,14 +378,14 @@ public:
/**
* Constructor
* @param z measurement
* @param key by which to look up X value in Values
* @param key1 by which to look up X value in Values
*/
NoiseModelFactor1(const SharedNoiseModel& noiseModel, Key key1) :
Base(noiseModel, key1) {}
/** Calls the 1-key specific version of evaluateError, which is pure virtual
* so must be implemented in the derived class. */
* so must be implemented in the derived class.
*/
virtual Vector unwhitenedError(const Values& x, boost::optional<std::vector<Matrix>&> H = boost::none) const {
if(this->active(x)) {
const X& x1 = x.at<X>(keys_[0]);

8
gtsam/nonlinear/Values.h
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@ -402,7 +402,7 @@ namespace gtsam {
* When iterating over the filtered view, only the key-value pairs
* with a key causing \c filterFcn to return \c true are visited. Because
* the object Filtered<Value> returned from filter() is only a
* <emph>view</emph> the original Values object must not be deallocated or
* <em>view</em> the original Values object must not be deallocated or
* go out of scope as long as the view is needed.
* @param filterFcn The function that determines which key-value pairs are
* included in the filtered view, for which this function returns \c true
@ -421,7 +421,7 @@ namespace gtsam {
* template argument \c ValueType and whose key causes the function argument
* \c filterFcn to return true are visited when iterating over the filtered
* view. Because the object Filtered<Value> returned from filter() is only
* a <emph>view</emph> the original Values object must not be deallocated or
* a <em>view</em> the original Values object must not be deallocated or
* go out of scope as long as the view is needed.
* @tparam ValueType The type that the value in a key-value pair must match
* to be included in the filtered view. Currently, base classes are not
@ -445,7 +445,7 @@ namespace gtsam {
* When iterating over the filtered view, only the key-value pairs
* with a key causing \c filterFcn to return \c true are visited. Because
* the object Filtered<Value> returned from filter() is only a
* <emph>view</emph> the original Values object must not be deallocated or
* <em>view</em> the original Values object must not be deallocated or
* go out of scope as long as the view is needed.
* @param filterFcn The function that determines which key-value pairs are
* included in the filtered view, for which this function returns \c true
@ -464,7 +464,7 @@ namespace gtsam {
* template argument \c ValueType and whose key causes the function argument
* \c filterFcn to return true are visited when iterating over the filtered
* view. Because the object Filtered<Value> returned from filter() is only
* a <emph>view</emph> the original Values object must not be deallocated or
* a <em>view</em> the original Values object must not be deallocated or
* go out of scope as long as the view is needed.
* @tparam ValueType The type that the value in a key-value pair must match
* to be included in the filtered view. Currently, base classes are not

2
gtsam/nonlinear/WhiteNoiseFactor.h
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@ -51,7 +51,7 @@ namespace gtsam {
public:
/** @brief negative log likelihood as a function of mean \mu and precision \tau
/** @brief negative log likelihood as a function of mean \f$ \mu \f$ and precision \f$ \tau \f$
* @f[
* f(z, \tau, \mu)
* = -\log \left( \frac{\sqrt{\tau}}{\sqrt{2\pi}} \exp(-0.5\tau(z-\mu)^2) \right)