https://wjngkoh.wordpress.com/2015/03/04/c-hash-function-for-eigen-matrix-and-vector/

@todo the threshold (u_judge_range) should not be a const value

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Expr ::= OrExpr OrExpr ::= AndExpr | OrExpr 'or' AndExpr AndExpr ::= EqualityExpr | AndExpr 'and' EqualityExpr EqualityExpr ::= RelationalExpr | Equal

FilterExpr ::= PrimaryExpr | FilterExpr Predicate Predicate ::= '[' PredicateExpr ']' PredicateExpr ::= Expr

LocationPath ::= RelativeLocationPath | AbsoluteLocationPath AbsoluteLocationPath ::= '/' RelativeLocationPath? | '//' RelativeLocationPath

PathExpr ::= LocationPath | FilterExpr | FilterExpr '/' RelativeLocationPath | FilterExpr '//' RelativeLocationPath UnionExpr ::= PathExpr | UnionExpr

PrimaryExpr ::= VariableReference | '(' Expr ')' | Literal | Number | FunctionCall

RelativeLocationPath ::= Step | RelativeLocationPath '/' Step | RelativeLocationPath '//' Step

Step ::= AxisSpecifier NodeTest Predicate* | AbbreviatedStep AxisSpecifier ::= AxisName '::' | '@'? NodeTest ::= NameTest | NodeType '(' ')' | 'proces

\brief Search for the closest nearest neighbor of a given point. * \param query the point to search a nearest neighbour for * \param i

\brief Set the number of neighbors used when selecting a point neighbourhood * to compute covariances. * A higher value will bring mo

\brief Set the minimum allowable ratio between eigenvalues to prevent singular covariance matrices. * \param[in] min_covar_eigvalue_mult the m

\brief Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to) * \param[in] target the input p

set maximum number of iterations at the optimization step * \param[in] max maximum number of iterations for the optimizer */

\brief Set the minimum number of points required for a cell to be used (must be 3 or greater for covariance calculation). * \param[in] min_poi

\brief Set/change the point cloud outlier ratio. * \param[in] outlier_ratio outlier ratio */

\brief Provide a pointer to the covariances of the input source (if computed externally!). * If not set, GeneralizedIterativeClosestPoint wil

\brief Provide a pointer to the covariances of the input target (if computed externally!). * If not set, GeneralizedIterativeClosestPoint wil