Point Cloud Library (PCL)  1.7.0
icp_nl.h
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40
41 #ifndef PCL_ICP_NL_H_
42 #define PCL_ICP_NL_H_
43
44 // PCL includes
45 #include <pcl/registration/icp.h>
46 #include <pcl/registration/transformation_estimation_lm.h>
47
48 namespace pcl
49 {
50  /** \brief @b IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization
51  * backend. The resultant transformation is optimized as a quaternion.
52  *
53  * The algorithm has several termination criteria:
54  *
55  * <ol>
56  * <li>Number of iterations has reached the maximum user imposed number of iterations
57  * (via \ref setMaximumIterations)</li>
58  * <li>The epsilon (difference) between the previous transformation and the current estimated transformation is
59  * smaller than an user imposed value (via \ref setTransformationEpsilon)</li>
60  * <li>The sum of Euclidean squared errors is smaller than a user defined threshold
61  * (via \ref setEuclideanFitnessEpsilon)</li>
62  * </ol>
63  *
64  * \author Radu B. Rusu, Michael Dixon
65  * \ingroup registration
66  */
67  template <typename PointSource, typename PointTarget, typename Scalar = float>
68  class IterativeClosestPointNonLinear : public IterativeClosestPoint<PointSource, PointTarget, Scalar>
69  {
74
75  public:
76
77  typedef boost::shared_ptr< IterativeClosestPointNonLinear<PointSource, PointTarget, Scalar> > Ptr;
78  typedef boost::shared_ptr< const IterativeClosestPointNonLinear<PointSource, PointTarget, Scalar> > ConstPtr;
79
81
82  /** \brief Empty constructor. */
84  {
86  reg_name_ = "IterativeClosestPointNonLinear";
87
89  }
90  };
91 }
92
93 #endif //#ifndef PCL_ICP_NL_H_