Pose Graph 3D
----------------
The Simultaneous Localization and Mapping (SLAM) problem consists of building a
map of an unknown environment while simultaneously localizing against this
map. The main difficulty of this problem stems from not having any additional
external aiding information such as GPS. SLAM has been considered one of the
fundamental challenges of robotics. A pose graph optimization problem is one
example of a SLAM problem.
The example also illustrates how to use Eigen's geometry module with Ceres'
automatic differentiation functionality. To represent the orientation, we will
use Eigen's quaternion which uses the Hamiltonian convention but has different
element ordering as compared with Ceres's rotation representation. Specifically
they differ by whether the scalar component q_w is first or last; the element
order for Ceres's quaternion is [q_w, q_x, q_y, q_z] where as Eigen's quaternion
is [q_x, q_y, q_z, q_w].
This package defines the necessary Ceres cost functions needed to model the
3-dimensional pose graph optimization problem as well as a binary to build and
solve the problem. The cost functions are shown for instruction purposes and can
be speed up by using analytical derivatives which take longer to implement.
Running
-----------
This package includes an executable `pose_graph_3d` that will read a problem
definition file. This executable can work with any 3D problem definition that
uses the g2o format with quaternions used for the orientation representation. It
would be relatively straightforward to implement a new reader for a different
format such as TORO or others. `pose_graph_3d` will print the Ceres solver full
summary and then output to disk the original and optimized poses
(`poses_original.txt` and `poses_optimized.txt`, respectively) of the robot in
the following format:
```
pose_id x y z q_x q_y q_z q_w
pose_id x y z q_x q_y q_z q_w
pose_id x y z q_x q_y q_z q_w
...
```
where `pose_id` is the corresponding integer ID from the file definition. Note,
the file will be sorted in ascending order for the `pose_id`.
The executable `pose_graph_3d` has one flag `--input` which is the path to the
problem definition. To run the executable,
```
/path/to/bin/pose_graph_3d --input /path/to/dataset/dataset.g2o
```
A script is provided to visualize the resulting output files. There is also an
option to enable equal axes using ```--axes_equal```.
```
/path/to/repo/examples/slam/pose_graph_3d/plot_results.py --optimized_poses ./poses_optimized.txt --initial_poses ./poses_original.txt
```
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ceres源码安装包,用于SLAM十四讲中的环境安装使用
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dat:27个
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ceres源码安装包,用于SLAM十四讲中的环境安装使用 (658个子文件)
problem_02.bin 285KB
problem_03.bin 123KB
problem_01.bin 102KB
BUILD 7KB
BUILD 4KB
eigen.BUILD 2KB
ceres.bzl 8KB
curve_fitting.c 6KB
gmock_gtest_all.cc 440KB
problem_test.cc 53KB
covariance_test.cc 41KB
line_search.cc 38KB
rotation_test.cc 35KB
solver.cc 34KB
problem_impl.cc 34KB
covariance_impl.cc 33KB
trust_region_minimizer.cc 30KB
libmv_bundle_adjuster.cc 28KB
local_parameterization_test.cc 27KB
more_garbow_hillstrom.cc 27KB
dogleg_strategy.cc 26KB
nist.cc 25KB
compressed_row_sparse_matrix.cc 24KB
dynamic_autodiff_cost_function_test.cc 24KB
reorder_program.cc 23KB
visibility_based_preconditioner.cc 23KB
jet_test.cc 23KB
evaluator_test.cc 23KB
gradient_checker_test.cc 22KB
line_search_minimizer.cc 19KB
autodiff_test.cc 19KB
compressed_row_sparse_matrix_test.cc 19KB
program.cc 18KB
numeric_diff_cost_function_test.cc 18KB
linear_least_squares_problems.cc 18KB
small_blas_test.cc 17KB
dynamic_numeric_diff_cost_function_test.cc 17KB
gradient_checking_cost_function_test.cc 16KB
cubic_interpolation_test.cc 16KB
solver_test.cc 16KB
line_search_direction.cc 15KB
ellipse_approximation.cc 15KB
block_sparse_matrix.cc 15KB
polynomial_test.cc 15KB
dynamic_sparsity_test.cc 15KB
trust_region_preprocessor.cc 15KB
trust_region_minimizer_test.cc 15KB
libmv_homography.cc 15KB
bundle_adjuster.cc 15KB
suitesparse.cc 14KB
schur_complement_solver.cc 14KB
sparse_cholesky_test.cc 14KB
program_test.cc 14KB
robot_pose_mle.cc 14KB
local_parameterization.cc 13KB
cost_function_to_functor_test.cc 13KB
inner_product_computer.cc 13KB
visibility_based_preconditioner_test.cc 13KB
evaluation_callback_test.cc 13KB
polynomial.cc 13KB
trust_region_preprocessor_test.cc 13KB
dogleg_strategy_test.cc 11KB
gradient_problem_solver.cc 11KB
gradient_checking_cost_function.cc 11KB
residual_block_test.cc 11KB
bal_problem.cc 11KB
gradient_checker.cc 11KB
inner_product_computer_test.cc 10KB
coordinate_descent_minimizer.cc 10KB
compressed_row_jacobian_writer.cc 10KB
dynamic_sparse_normal_cholesky_solver.cc 10KB
schur_complement_solver_test.cc 10KB
triplet_sparse_matrix.cc 9KB
types.cc 9KB
cxsparse.cc 9KB
reorder_program_test.cc 9KB
accelerate_sparse.cc 9KB
parallel_for_cxx.cc 9KB
parameter_block_test.cc 9KB
corrector_test.cc 9KB
triplet_sparse_matrix_test.cc 9KB
numeric_diff_test_utils.cc 9KB
loss_function_test.cc 9KB
compressed_col_sparse_matrix_utils_test.cc 8KB
residual_block.cc 8KB
block_jacobian_writer.cc 8KB
system_test.cc 8KB
schur_eliminator_test.cc 8KB
detect_structure_test.cc 8KB
canonical_views_clustering.cc 8KB
denoising.cc 8KB
conjugate_gradients_solver.cc 8KB
subset_preconditioner_test.cc 8KB
eigensparse.cc 8KB
low_rank_inverse_hessian.cc 8KB
implicit_schur_complement.cc 8KB
simple_bundle_adjuster.cc 8KB
concurrent_queue_test.cc 8KB
implicit_schur_complement_test.cc 7KB
block_sparse_matrix_test.cc 7KB
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