- Eigen/SparseCore is required by covariance_impl, this was added in
v 3.1.0 of Eigen, and thus without at least this version Ceres will
not compile.
- Note that Ubuntu 12.04 provides only version 3.0.5 in the mainline
repository.
- Update docs to match CMake check for Eigen >= 3.2.2 to avoid warning
about reduced sparse performance.
Change-Id: I291bb185d1c76e1e1422429169a76e3f1b828163
This adds a new wrapper class called DynamicCostFunctionToFunctor
that closes a gap in the current API: the existing
CostFunctionToFunctor can only be used with a SizedCostFunction, where
the number and sizes of all parameter vectors are known at compile-time.
The DynamicCostFunctionToFunctor allows you to wrap a generic
CostFunction into a templated functor which can then be used in a
DynamicAutoDiffCostFunction.
Also updates the existing CostFunctionToFunctor class to internally use
DynamicCostFunctionToFunctor.
Change-Id: I088adc3271c58d2519126c27037c3576965a36d6
- Include example use of new find_dependency() macro in CMake 3.x to
find dependencies in <Project>Config.cmake files.
- Also fix typo in NNLS modeling docs.
Change-Id: Ie9862b69c0451ee8775826f2957f5e182d937439
- A lot of users find the CMake package installation process confusing,
particularly the use of imported targets when exporting their own
projects which use Ceres.
- This patch adds a brief description of the overall process and the
main bear-trap users are ensnared by.
Change-Id: I44c022bbd18a393868bf88ea9ddd807c5e08abc9
1. Add documentation for cubic_interpolation.h
2. Remove the list of publications. It is an incomplete list which is
a pain to maintain.
3. Add a note about the interaction between manifolds and
NumericDiffCostFunction.
4. Fix some of the comments in cubic_interpolation.h to better reflect reality.
5. Updated the version history.
Change-Id: I0b4a5a6f3361d3fc85f1b4aec685cd80540934f1
Since Ceres is moving to using GitHub for issues, and the Google
Code URL in the current copyright header will soon become invalid,
update all the headers.
Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
This CL is required to build Tango.
Inspired by this commit in RedwoodInternal repository:
commit 09dde53c248e04f432b5eccceea5daeedb706aea
Author: Mike Vitus <mike@hidof.com>
Date: Wed Apr 23 11:05:17 2014 -0700
Change-Id: I328b6634969de4ccdd71947945aa67a49ee9073f
The example code for DynamicNumericDiffCostFunction and
DynamicAutoDiffCostFunction in the documentation was
allocating the cost function objects on the stack rather
than the heap.
Thanks to Rodney Hoskinson for reporting this.
Change-Id: I217aee02d1e2c1e9c25b8197451e9f9e0482915d
- Previously we had no defined default value for
sparse_linear_algebra_library_type in Solver::Options if Ceres
was compiled with no sparse library available. Thus in that case,
the default value (dependent upon the compiler) would indicate that
one was available.
- Now we have an explicit option that means no sparse library is
available, which is now the default value in Solver::Options in this
case.
- Add a warning in CMake when the user disables all sparse libraries.
- Fix typos in trust_region_preprocessor_test:
(SUITE/CX)_SPARSE -> (SUITE/CX)SPARSE that induced failures when
no sparse libraries were available.
Change-Id: I869c399a12d42bfc44220cbb25ce6d6dd80236bd
Use Eigen's much more complicated conversion routine
when we encounter cases where the angle of rotation is
close to Pi.
Along the way also fix the way angle_axis vectors are
compared by making the matcher more robust.
Thanks to Tobias Strauss for reporting this.
Change-Id: Ia7e65dafad92c48d29d5f3cd22c4d6534789c183
1. Add information about homebrew/science to the docs.
2. Update version history.
3. Update RPM spec file.
Change-Id: I800b81f9f2c8733a8add338cc0a205b7b8bc8573
1. Fix the release script to ignore the version.h checking.
2. Fix some ceres documentation formatting errors.
Change-Id: I3fd6b85e771b242f463d6a36c3efd8d691f9242f
1. Complete restructuring of the documentation to account for
GradientProblemSolver.
2. Update the version history to account for changes since 1.9.0.
3. Add links and document the various examples that ship with ceres.
4. Documentation for GradientProblem GradientProblemSolver.
Change-Id: If3a18f2850cbc98be1bc34435e9ea468785b8b27
Its API was broken, and its implementation was an unnecessary
layer of abstraction over CostFunctionToFunctor.
Change-Id: I18fc261fc6a3620b51a9eeb4dde0af03d753af69
1. Fix a build breakage in graph_test.
2. Respect Solver::Options::min_num_linear_solver_iterations in
conjugate_gradients_solver.cc
Thanks to Johannes Schönberger for reporting these.
Change-Id: Ib32e3929bf5d92dd576ae5b53d4d88797095136e
Replace SolverImpl with
a. A minimizer specific preprocessor class.
b. A generic Solve function inside solver.cc
c. Presummarize and Postsummarize functions to handle
updates to the summary object.
The existing SolverImpl class was a mixture of the above three
things and was increasingly complicated code to follow. This change,
breaks it into its three separate constituents, with the aims of
better separation of concerns and thus better testability and
reliability.
The call to Solver::Solve() now consists of
1. Presummarize - summarize the given state of the problem and solver
options.
2. Preprocess - Setup everything that is needed to call the minimizer.
This includes, removing redundant parameter and residual blocks,
setting up the reordering for the linear solver, creating the
linear solver, evaluator, inner iteration minimizer etc.
3. Minimize.
4. Post summarize - summarize the result of the preprocessing and the
solve.
Change-Id: I80f35cfc9f2cbf78f1df4aceace27075779d8a3a
SPARSE_NORMAL_CHOLESKY and SPARSE_SCHUR can now be used
with EIGEN_SPARSE as the backend.
The performance is not as good as CXSparse. This needs to be
investigated. Is it because the quality of AMD ordering that
we are computing is not as good as the one for CXSparse? This
could be because we are working with the scalar matrix instead
of the block matrix.
Also, the upper/lower triangular story is not completely clear.
Both of these issues will be benchmarked and tackled in the
near future.
Also included in this change is a bunch of cleanup to the
SparseNormalCholeskySolver and SparseSchurComplementSolver
classes around the use of the of defines used to conditionally
compile out parts of the code.
The system_test has been updated to test EIGEN_SPARSE also.
Change-Id: I46a57e9c4c97782696879e0b15cfc7a93fe5496a
Sparse Cholesky factorization is not rank revealing. Therefore
this algorithm cannot reliably tell when the Jacobian matrix is
rank deficient or so poorly conditioned that the covariance matrix
cannot be estimated.
Making things worse, this algorithm works on the normal equations,
which makes the conditioning problem much worse.
This change, deletes the SPARSE_CHOLESKY algorithm in the covariance
estimation code. Also to make the naming consistent, it renames
SPARSE_QR -> SUITE_SPARSE_QR
so that it parallels EIGEN_SPARSE_QR.
Also, since we now have EIGEN_SPARSE_QR, we can default to using
it when SuiteSparse is not available instead of DENSE_SVD, which
generally speaking should only be used by folks who are dealing
with small rank deficient jacobians.
Change-Id: I8b134c7e8a2e86ca374371f185b19f1c3e74349c
Alessandro Gentilini pointed out that the install instructions
don't actually install the library.
Also included are some other minor changes to dependency
description.
Change-Id: I50872114bca7a9774ef66f55ba78ed9b7adad93f
Alex Stewart