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Doxygen workaround in JacobianFactor documetation

release/4.3a0
Richard Roberts 2011-11-05 21:26:38 +00:00
parent bfb0bb0b4a
commit 66b0b2c021
1 changed files with 3 additions and 3 deletions
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6
gtsam/linear/JacobianFactor.h
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@ -56,11 +56,11 @@ namespace gtsam {
*
* Letting \f$ h(x) \f$ be a \a linear measurement prediction function, \f$ z \f$ be
* the actual observed measurement, the residual is
* \f[ f(x) = h(x) - z \text{.} \f]
* \f[ f(x) = h(x) - z . \f]
* If we expect noise with diagonal covariance matrix \f$ \Sigma \f$ on this
* measurement, then the negative log-likelihood of the Gaussian induced by this
* measurement model is
* \f[ E(x) = \frac{1}{2} (h(x) - z)^T \Sigma^{-1} (h(x) - z) \text. \f]
* \f[ E(x) = \frac{1}{2} (h(x) - z)^T \Sigma^{-1} (h(x) - z) . \f]
* Because \f$ h(x) \f$ is linear, we can write it as
* \f[ h(x) = Ax + e \f]
* and thus we have
@ -75,7 +75,7 @@ namespace gtsam {
* for example, for a 2-way factor, the constructor would accept \f$ A1 \f$ and \f$ A2 \f$,
* as well as the variable indices \f$ j1 \f$ and \f$ j2 \f$
* and the negative log-likelihood represented by this factor would be
* \f[ E(x) = \frac{1}{2} (A_1 x_{j1} + A_2 x_{j2} - b)^T \Sigma^{-1} (A_1 x_{j1} + A_2 x_{j2} - b) \text{.} \f]
* \f[ E(x) = \frac{1}{2} (A_1 x_{j1} + A_2 x_{j2} - b)^T \Sigma^{-1} (A_1 x_{j1} + A_2 x_{j2} - b) . \f]
*/
class JacobianFactor : public GaussianFactor {
public: