Applied clang

Signed-off-by: Anh Nguyen <[email protected]>

include/multigrid_pclomp/multigrid_ndt_omp.h

@@ -125,7 +125,9 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
     MultiGridNormalDistributionsTransform && other) noexcept;
 
   /** \brief Empty destructor */
-  virtual ~MultiGridNormalDistributionsTransform() {}
+  virtual ~MultiGridNormalDistributionsTransform()
+  {
+  }
 
   void setNumThreads(int n)
   {
@@ -134,7 +136,10 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
     target_cells_.setThreadNum(params_.num_threads);
   }
 
-  inline int getNumThreads() const { return params_.num_threads; }
+  inline int getNumThreads() const
+  {
+    return params_.num_threads;
+  }
 
   inline void setInputSource(const PointCloudSourceConstPtr & input)
   {
@@ -165,7 +170,10 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
     target_cells_.setInputCloudAndFilter(cloud, target_id);
   }
 
-  inline void removeTarget(const std::string target_id) { target_cells_.removeCloud(target_id); }
+  inline void removeTarget(const std::string target_id)
+  {
+    target_cells_.removeCloud(target_id);
+  }
 
   inline void createVoxelKdtree()
   {
@@ -188,32 +196,50 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
   /** \brief Get voxel grid resolution.
    * \return side length of voxels
    */
-  inline float getResolution() const { return (params_.resolution); }
+  inline float getResolution() const
+  {
+    return (params_.resolution);
+  }
 
   /** \brief Get the newton line search maximum step length.
    * \return maximum step length
    */
-  inline double getStepSize() const { return (params_.step_size); }
+  inline double getStepSize() const
+  {
+    return (params_.step_size);
+  }
 
   /** \brief Set/change the newton line search maximum step length.
    * \param[in] step_size maximum step length
    */
-  inline void setStepSize(double step_size) { params_.step_size = step_size; }
+  inline void setStepSize(double step_size)
+  {
+    params_.step_size = step_size;
+  }
 
   /** \brief Get the point cloud outlier ratio.
    * \return outlier ratio
    */
-  inline double getOutlierRatio() const { return (outlier_ratio_); }
+  inline double getOutlierRatio() const
+  {
+    return (outlier_ratio_);
+  }
 
   /** \brief Set/change the point cloud outlier ratio.
    * \param[in] outlier_ratio outlier ratio
    */
-  inline void setOutlierRatio(double outlier_ratio) { outlier_ratio_ = outlier_ratio; }
+  inline void setOutlierRatio(double outlier_ratio)
+  {
+    outlier_ratio_ = outlier_ratio;
+  }
 
   /** \brief Get the registration alignment probability.
    * \return transformation probability
    */
-  inline double getTransformationProbability() const { return (trans_probability_); }
+  inline double getTransformationProbability() const
+  {
+    return (trans_probability_);
+  }
 
   inline double getNearestVoxelTransformationLikelihood() const
   {
@@ -223,10 +249,16 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
   /** \brief Get the number of iterations required to calculate alignment.
    * \return final number of iterations
    */
-  inline int getFinalNumIteration() const { return (nr_iterations_); }
+  inline int getFinalNumIteration() const
+  {
+    return (nr_iterations_);
+  }
 
   /** \brief Return the hessian matrix */
-  inline Eigen::Matrix<double, 6, 6> getHessian() const { return hessian_; }
+  inline Eigen::Matrix<double, 6, 6> getHessian() const
+  {
+    return hessian_;
+  }
 
   /** \brief Return the transformation array */
   inline const std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f>> &
@@ -273,7 +305,10 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
     regularization_pose_ = regularization_pose;
   }
 
-  inline void unsetRegularizationPose() { regularization_pose_ = boost::none; }
+  inline void unsetRegularizationPose()
+  {
+    regularization_pose_ = boost::none;
+  }
 
   NdtResult getResult()
   {
@@ -296,22 +331,34 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
    * transformation epsilon in order for an optimization to be considered as having
    * converged to the final solution.
    */
-  inline void setTransformationEpsilon(double epsilon) { params_.trans_epsilon = epsilon; }
+  inline void setTransformationEpsilon(double epsilon)
+  {
+    params_.trans_epsilon = epsilon;
+  }
 
   /** \brief Get the transformation epsilon (maximum allowable translation squared
    * difference between two consecutive transformations) as set by the user.
    */
-  inline double getTransformationEpsilon() { return (params_.trans_epsilon); }
+  inline double getTransformationEpsilon()
+  {
+    return (params_.trans_epsilon);
+  }
 
   inline void setMaximumIterations(int max_iterations)
   {
     params_.max_iterations = max_iterations;
     max_iterations_ = params_.max_iterations;
   }
 
-  inline int getMaxIterations() const { return params_.max_iterations; }
+  inline int getMaxIterations() const
+  {
+    return params_.max_iterations;
+  }
 
-  inline int getMaxIterations() { return params_.max_iterations; }
+  inline int getMaxIterations()
+  {
+    return params_.max_iterations;
+  }
 
   void setParams(const NdtParams & ndt_params)
   {
@@ -321,11 +368,20 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
     target_cells_.setThreadNum(params_.num_threads);
   }
 
-  NdtParams getParams() const { return params_; }
+  NdtParams getParams() const
+  {
+    return params_;
+  }
 
-  pcl::PointCloud<PointTarget> getVoxelPCD() const { return target_cells_.getVoxelPCD(); }
+  pcl::PointCloud<PointTarget> getVoxelPCD() const
+  {
+    return target_cells_.getVoxelPCD();
+  }
 
-  std::vector<std::string> getCurrentMapIDs() const { return target_cells_.getCurrentMapIDs(); }
+  std::vector<std::string> getCurrentMapIDs() const
+  {
+    return target_cells_.getCurrentMapIDs();
+  }
 
 protected:
   using BaseRegType::converged_;
@@ -357,7 +413,9 @@ class MultiGridNormalDistributionsTransform : public pcl::Registration<PointSour
   virtual void computeTransformation(PointCloudSource & output, const Eigen::Matrix4f & guess);
 
   /** \brief Initiate covariance voxel structure. */
-  void inline init() {}
+  void inline init()
+  {
+  }
 
   /** \brief Compute derivatives of probability function w.r.t. the transformation vector.
    * \note Equation 6.10, 6.12 and 6.13 [Magnusson 2009].

include/pclomp/gicp_omp.h

@@ -217,33 +217,51 @@ class GeneralizedIterativeClosestPoint : public pcl::IterativeClosestPoint<Point
    * converged to the final solution.
    * \param epsilon the rotation epsilon
    */
-  inline void setRotationEpsilon(double epsilon) { rotation_epsilon_ = epsilon; }
+  inline void setRotationEpsilon(double epsilon)
+  {
+    rotation_epsilon_ = epsilon;
+  }
 
   /** \brief Get the rotation epsilon (maximum allowable difference between two
    * consecutive rotations) as set by the user.
    */
-  inline double getRotationEpsilon() { return (rotation_epsilon_); }
+  inline double getRotationEpsilon()
+  {
+    return (rotation_epsilon_);
+  }
 
   /** \brief Set the number of neighbors used when selecting a point neighbourhood
    * to compute covariances.
    * A higher value will bring more accurate covariance matrix but will make
    * covariances computation slower.
    * \param k the number of neighbors to use when computing covariances
    */
-  void setCorrespondenceRandomness(int k) { k_correspondences_ = k; }
+  void setCorrespondenceRandomness(int k)
+  {
+    k_correspondences_ = k;
+  }
 
   /** \brief Get the number of neighbors used when computing covariances as set by
    * the user
    */
-  int getCorrespondenceRandomness() { return (k_correspondences_); }
+  int getCorrespondenceRandomness()
+  {
+    return (k_correspondences_);
+  }
 
   /** set maximum number of iterations at the optimization step
    * \param[in] max maximum number of iterations for the optimizer
    */
-  void setMaximumOptimizerIterations(int max) { max_inner_iterations_ = max; }
+  void setMaximumOptimizerIterations(int max)
+  {
+    max_inner_iterations_ = max;
+  }
 
   ///\return maximum number of iterations at the optimization step
-  int getMaximumOptimizerIterations() { return (max_inner_iterations_); }
+  int getMaximumOptimizerIterations()
+  {
+    return (max_inner_iterations_);
+  }
 
 protected:
   /** \brief The number of neighbors used for covariances computation.