mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
02706c1906
Add a Covariance object to the API. Given a Problem object and a set of parameter block pairs the Covariance object computes a sparse covariance matrix corresponding to those block pairs and provides random access to them. Constant parameter blocks and parameter blocks with local parameterizations are correctly handled. Sparse and dense implementations are provided. With the dense implementation rank deficient Jacobians can also be handled. Parts of the code are threaded using OpenMP if available. Change-Id: I5b49583b3d79579df3e0f334c22567acb23ed4ad
194 lines
8.6 KiB
C++
194 lines
8.6 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: keir@google.com (Keir Mierle)
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//
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// This is the implementation of the public Problem API. The pointer to
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// implementation (PIMPL) idiom makes it possible for Ceres internal code to
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// refer to the private data members without needing to exposing it to the
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// world. An alternative to PIMPL is to have a factory which returns instances
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// of a virtual base class; while that approach would work, it requires clients
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// to always put a Problem object into a scoped pointer; this needlessly muddies
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// client code for little benefit. Therefore, the PIMPL comprise was chosen.
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#ifndef CERES_PUBLIC_PROBLEM_IMPL_H_
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#define CERES_PUBLIC_PROBLEM_IMPL_H_
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#include <map>
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#include <vector>
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#include "ceres/internal/macros.h"
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#include "ceres/internal/port.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/problem.h"
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#include "ceres/types.h"
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namespace ceres {
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class CostFunction;
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class LossFunction;
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class LocalParameterization;
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struct CRSMatrix;
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namespace internal {
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class Program;
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class ResidualBlock;
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class ProblemImpl {
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public:
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typedef map<double*, ParameterBlock*> ParameterMap;
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ProblemImpl();
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explicit ProblemImpl(const Problem::Options& options);
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~ProblemImpl();
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// See the public problem.h file for description of these methods.
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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const vector<double*>& parameter_blocks);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2,
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double* x3);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2,
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double* x3, double* x4);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2,
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double* x3, double* x4, double* x5);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2,
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double* x3, double* x4, double* x5,
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double* x6);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2,
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double* x3, double* x4, double* x5,
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double* x6, double* x7);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2,
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double* x3, double* x4, double* x5,
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double* x6, double* x7, double* x8);
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ResidualBlockId AddResidualBlock(CostFunction* cost_function,
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LossFunction* loss_function,
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double* x0, double* x1, double* x2,
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double* x3, double* x4, double* x5,
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double* x6, double* x7, double* x8,
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double* x9);
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void AddParameterBlock(double* values, int size);
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void AddParameterBlock(double* values,
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int size,
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LocalParameterization* local_parameterization);
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void RemoveResidualBlock(ResidualBlock* residual_block);
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void RemoveParameterBlock(double* values);
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void SetParameterBlockConstant(double* values);
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void SetParameterBlockVariable(double* values);
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void SetParameterization(double* values,
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LocalParameterization* local_parameterization);
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bool Evaluate(const Problem::EvaluateOptions& options,
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double* cost,
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vector<double>* residuals,
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vector<double>* gradient,
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CRSMatrix* jacobian);
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int NumParameterBlocks() const;
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int NumParameters() const;
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int NumResidualBlocks() const;
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int NumResiduals() const;
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int ParameterBlockSize(const double* parameter_block) const;
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int ParameterBlockLocalSize(const double* parameter_block) const;
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void GetParameterBlocks(vector<double*>* parameter_blocks) const;
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const Program& program() const { return *program_; }
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Program* mutable_program() { return program_.get(); }
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const ParameterMap& parameter_map() const { return parameter_block_map_; }
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private:
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ParameterBlock* InternalAddParameterBlock(double* values, int size);
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bool InternalEvaluate(Program* program,
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double* cost,
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vector<double>* residuals,
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vector<double>* gradient,
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CRSMatrix* jacobian);
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// Delete the arguments in question. These differ from the Remove* functions
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// in that they do not clean up references to the block to delete; they
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// merely delete them.
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template<typename Block>
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void DeleteBlockInVector(vector<Block*>* mutable_blocks,
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Block* block_to_remove);
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void DeleteBlock(ResidualBlock* residual_block);
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void DeleteBlock(ParameterBlock* parameter_block);
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const Problem::Options options_;
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// The mapping from user pointers to parameter blocks.
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map<double*, ParameterBlock*> parameter_block_map_;
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// The actual parameter and residual blocks.
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internal::scoped_ptr<internal::Program> program_;
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// When removing residual and parameter blocks, cost/loss functions and
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// parameterizations have ambiguous ownership. Instead of scanning the entire
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// problem to see if the cost/loss/parameterization is shared with other
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// residual or parameter blocks, buffer them until destruction.
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//
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// TODO(keir): See if it makes sense to use sets instead.
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vector<CostFunction*> cost_functions_to_delete_;
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vector<LossFunction*> loss_functions_to_delete_;
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vector<LocalParameterization*> local_parameterizations_to_delete_;
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CERES_DISALLOW_COPY_AND_ASSIGN(ProblemImpl);
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};
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} // namespace internal
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} // namespace ceres
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#endif // CERES_PUBLIC_PROBLEM_IMPL_H_
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