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update doxygen (review comment)

release/4.3a0
Gerry Chen 2021-12-09 02:30:51 -05:00
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@ -16,7 +16,7 @@ To use GTSAM to solve your own problems, you will often have to create new facto
-# The number of variables your factor involves is <b>unknown</b> at compile time - derive from NoiseModelFactor and implement NoiseModelFactor::unwhitenedError()
- This is a factor expressing the sum-of-squares error between a measurement \f$ z \f$ and a measurement prediction function \f$ h(x) \f$, on which the errors are expected to follow some distribution specified by a noise model (see noiseModel).
-# The number of variables your factor involves is <b>known</b> at compile time and is between 1 and 6 - derive from NoiseModelFactor1, NoiseModelFactor2, NoiseModelFactor3, NoiseModelFactor4, NoiseModelFactor5, or NoiseModelFactor6, and implement <b>\c evaluateError()</b>. If the number of variables is greater than 6, derive from NoiseModelFactorN.
-# The number of variables your factor involves is <b>known</b> at compile time, derive from NoiseModelFactorN<T1, T2, ...> (where T1, T2, ... are the types of the variables, e.g. double, Vector, Pose3) and implement <b>\c evaluateError()</b>.
- This factor expresses the same sum-of-squares error with a noise model, but makes the implementation task slightly easier than with %NoiseModelFactor.
-# Derive from NonlinearFactor
- This is more advanced and allows creating factors without an explicit noise model, or that linearize to HessianFactor instead of JacobianFactor.