#include <mrpt/slam/CConsistentObservationAlignment.h>
Classes | |
struct | TOptions |
The options for the method. More... | |
Public Member Functions | |
CConsistentObservationAlignment () | |
void | execute (CSensFrameProbSequence &inputMap, CSensFrameProbSequence &outputMap) |
Executes the algorithm. | |
std::string | getAlgorithmName () |
A textual description for the implemented algorithm. | |
Static Public Member Functions | |
static void | optimizeUserSuppliedData (math::CMatrixTemplateObjects< CPosePDFGaussian > &in_PoseConstraints, math::CMatrixTemplateObjects< CPosePDFGaussian > &out_OptimalPoses) |
This alternate method provides the basic consistent alignment algorithm to any user-supplied matrix of pose constrainsts, returning the optimal poses of all the nodes relative to the first one. | |
Public Attributes | |
struct MRPTDLLIMPEXP mrpt::slam::CConsistentObservationAlignment::TOptions | options |
The options for the method. | |
Protected Types | |
typedef std::vector < CPosePDFGaussian > | vector_posesPdf |
A sequence of probabilistic poses:. |
This algorithm is based on the work of Lu & Milios [Globally Consistent Range Scan Alignment for Environment Mapping, 1997] for a global optimal estimation of laser range scan poses, but in this case it has been extended to include any type of observations as long as points-map-like operators are implemented over them.
This class work in the following way:
The input is a set of observations with associated "global" poses. This is supplied with a "CSensFrameProbSequence" object, but the probabilistic poses are ignored since only the mean values for the pose of each node are taken.
After invoking the algorithm with CConsistentObservationAlignment::execute(), a new "CSensFrameProbSequence" object is returned, where the NOTE: The first node on the input map is used as reference and therefore its pose is the only one which will never change.
Definition at line 67 of file CConsistentObservationAlignment.h.
typedef std::vector<CPosePDFGaussian> mrpt::slam::CConsistentObservationAlignment::vector_posesPdf [protected] |
A sequence of probabilistic poses:.
Definition at line 72 of file CConsistentObservationAlignment.h.
mrpt::slam::CConsistentObservationAlignment::CConsistentObservationAlignment | ( | ) |
void mrpt::slam::CConsistentObservationAlignment::execute | ( | CSensFrameProbSequence & | inputMap, | |
CSensFrameProbSequence & | outputMap | |||
) |
Executes the algorithm.
See description in CConsistentObservationAlignment.
inputMap | The input to the algorithm: a set of nodes situated (with global coordinates) and observations from each node. | |
outputMap | The globally consistent map, where probabilitic poses are filled with gaussian PDFs, where the mean is the globally optimal estimation and the covariance is also computed. |
std::string mrpt::slam::CConsistentObservationAlignment::getAlgorithmName | ( | ) |
A textual description for the implemented algorithm.
static void mrpt::slam::CConsistentObservationAlignment::optimizeUserSuppliedData | ( | math::CMatrixTemplateObjects< CPosePDFGaussian > & | in_PoseConstraints, | |
math::CMatrixTemplateObjects< CPosePDFGaussian > & | out_OptimalPoses | |||
) | [static] |
This alternate method provides the basic consistent alignment algorithm to any user-supplied matrix of pose constrainsts, returning the optimal poses of all the nodes relative to the first one.
in_PoseConstraints | This is a NxN matrix where element M(i,j) is the pose constrainst between node "i" and "j". Please, fill out only the upper-triangle part of the matrix (diagonal and lowe-part entries are not used). | |
out_OptimalPoses | The 1xN vector with the consistent global poses of all nodes, where the first node is always at (0,0,0deg). |
struct MRPTDLLIMPEXP mrpt::slam::CConsistentObservationAlignment::TOptions mrpt::slam::CConsistentObservationAlignment::options |
The options for the method.
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