1. Add abseil-cpp as a submodule. We are tracking the latest LTS
release, which is lts_2024_01_16.
2. Replace glog/gflags with absl::log and absl::flags.
3. Remove miniglog
4. Also take a whack at making the bazel build work with
abseil-cpp and gtest.
There are a number of TODOs in this CL that still need to be resolved.
Change-Id: I39355ed7d61375be4ebcbc8596d9cc70acc1c678
1. Add a version history
2. Update copyright years across the code base
3. Run format_all.sh
4. Update version strings from 2.1.0 to 2.2.0 in the docs and
elsewhere.
Change-Id: I46d8d479d54bd6002d532785e67342106e73c9ac
- Change formatting standard to Cpp11. Main difference is not having
the space between two closing >> for nested templates. We don't
choose c++14, because older versions of clang-format (version 9
and earlier) don't know this value yet, and it doesn't make a
difference in the formatting.
- Apply clang-format to all (non generated) internal source files.
- Manually fix some code sections (clang-format on/off) and c-strings
- Exclude some embedded external files with very different formatting
(gtest/gmock)
- Add script to format all source files
Change-Id: Ic6cea41575ad6e37c9e136dbce176b0d505dc44d
For proper alignment on the heap Eigen needs to have a custom
allocator. There are two forms, new and in-place new. To make sure
that memory is aligned using new, one needs to overload new by
adding EIGEN_MAKE_ALIGNED_OPERATOR_NEW to any struct which contains a
fixed size Eigen type either through inheritance or as a direct or
indirect member. For the in-place new one need to use the
Eigen::aligned_allocator (e.g. for std::vector, std::list,
FixedArray, etc.). For more details see:
https://eigen.tuxfamily.org/dox/group__DenseMatrixManipulation__Alignement.html
This CL adds EIGEN_MAKE_ALIGNED_OPERATOR_NEW to all structs, which
contain fixed-size Eigen types and uses the Eigen::aligned_allocator
for containers which stores structs of fixed-size Eigen types.
Change-Id: I06c6c4fc74a6835918d5d1c571b7814a14c029d8
1. Push the boundary handling logic into the underlying array
object. This has two very significant impacts:
a. The interpolation code becomes extremely simple to write
and to test.
b. The user has more flexibility in implementing how out of bounds
values are handled. We provide one default implementation.
Change-Id: Ic2f6cf9257ce7110c62e492688e5a6c8be1e7df2
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
The key change is that there is a new layer of abstract,
a Array object that the interpolator depends on.
The Array provides a one dimension or two dimensional
array like interface independent of the underlying representation
of the data.
Also included here is support for vector valued functions.
Change-Id: Ica68f03778cf0d84192db00cd55653f8b4124d51
This bi-cubic interpolation implementation is based
on the cubic convolution algorithm of keys, which allows
us to implement a bi-cubic spline like interpolation scheme
using five one dimensional cubic spline operations.
Change-Id: I116aa8036191c3e654af788323fc8298ae8252a6
Add a cubic interpolator based on the Catmull-Rom spline,
with support for automatic differentiation.
Change-Id: I02ae4c4ea37805ff1f717b05ea805989b474bd59
Sergiu Deitsch