From 11fd8612269690c9d95eba4a80ac5db705906aa1 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 9 Dec 2021 02:30:51 -0500 Subject: [PATCH] update doxygen (review comment) --- gtsam/mainpage.dox | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtsam/mainpage.dox b/gtsam/mainpage.dox index e07f45f07..e9c83da8a 100644 --- a/gtsam/mainpage.dox +++ b/gtsam/mainpage.dox @@ -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 unknown 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 known at compile time and is between 1 and 6 - derive from NoiseModelFactor1, NoiseModelFactor2, NoiseModelFactor3, NoiseModelFactor4, NoiseModelFactor5, or NoiseModelFactor6, and implement \c evaluateError(). If the number of variables is greater than 6, derive from NoiseModelFactorN. +-# The number of variables your factor involves is known at compile time, derive from NoiseModelFactorN (where T1, T2, ... are the types of the variables, e.g. double, Vector, Pose3) and implement \c evaluateError(). - 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.