| // Ceres Solver - A fast non-linear least squares minimizer | |
| // Copyright 2019 Google Inc. All rights reserved. | |
| // http://ceres-solver.org/ | |
| // | |
| // Redistribution and use in source and binary forms, with or without | |
| // modification, are permitted provided that the following conditions are met: | |
| // | |
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| // this list of conditions and the following disclaimer in the documentation | |
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| // specific prior written permission. | |
| // | |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
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| // | |
| // Author: sameeragarwal@google.com (Sameer Agarwal) | |
| // | |
| // Cost term that implements a prior on a parameter block using a | |
| // normal distribution. | |
| namespace ceres { | |
| // Implements a cost function of the form | |
| // | |
| // cost(x) = ||A(x - b)||^2 | |
| // | |
| // where, the matrix A and the vector b are fixed and x is the | |
| // variable. In case the user is interested in implementing a cost | |
| // function of the form | |
| // | |
| // cost(x) = (x - mu)^T S^{-1} (x - mu) | |
| // | |
| // where, mu is a vector and S is a covariance matrix, then, A = | |
| // S^{-1/2}, i.e the matrix A is the square root of the inverse of the | |
| // covariance, also known as the stiffness matrix. There are however | |
| // no restrictions on the shape of A. It is free to be rectangular, | |
| // which would be the case if the covariance matrix S is rank | |
| // deficient. | |
| class CERES_EXPORT NormalPrior final : public CostFunction { | |
| public: | |
| // Check that the number of rows in the vector b are the same as the | |
| // number of columns in the matrix A, crash otherwise. | |
| NormalPrior(const Matrix& A, const Vector& b); | |
| bool Evaluate(double const* const* parameters, | |
| double* residuals, | |
| double** jacobians) const override; | |
| private: | |
| Matrix A_; | |
| Vector b_; | |
| }; | |
| } // namespace ceres | |