NeroBurner
a548766d14
Update the usage of Google Flags (gflags) library the same way Glog updated it [1]. This pushes the minimum required gflags version to v2.2.0. Remove the ceres specific define of CERES_GFLAGS_NAMESPACE and directly use GFLAGS_NAMESPACE defined in gflags/gflags_declare.h [2]. In CeresConfig.cmake the hard coded paths for gflags are ommited. Instead we rely on the gflagsConfig file to get closer to a relocatable CeresConfig.cmake. Furthermore use the find_dependency() [4] cmake function specifically created for cmake-config files. This change builds upon the explicit PUBLIC/PRIVATE link change [3]. [1] https://github.com/google/glog/pull/199 [2] https://github.com/gflags/gflags/blob/d9b184bd0026b16bb4c2fded75d56fb2cce50d66/src/gflags_declare.h.in#L43 [3] https://ceres-solver-review.googlesource.com/c/ceres-solver/+/16220 [4] https://cmake.org/cmake/help/latest/module/CMakeFindDependencyMacro.html Change-Id: I9861a2699f2702bf1a5e99d07863a7e6639b7c39
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