Fix various errors and typos in the docstrings and tutorials

common/include/pcl/common/centroid.h

@@ -56,9 +56,9 @@ namespace pcl
   /** \brief Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
     * \param[in] cloud_iterator an iterator over the input point cloud
     * \param[out] centroid the output centroid
-    * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.
     * \note if return value is 0, the centroid is not changed, thus not valid.
-    * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices.
+    * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -82,9 +82,9 @@ namespace pcl
   /** \brief Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
     * \param[in] cloud the input point cloud
     * \param[out] centroid the output centroid
-    * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.
     * \note if return value is 0, the centroid is not changed, thus not valid.
-    * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices.
+    * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -110,9 +110,9 @@ namespace pcl
     * \param[in] cloud the input point cloud
     * \param[in] indices the point cloud indices that need to be used
     * \param[out] centroid the output centroid
-    * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input indices.
     * \note if return value is 0, the centroid is not changed, thus not valid.
-    * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices.
+    * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -141,9 +141,9 @@ namespace pcl
     * \param[in] cloud the input point cloud
     * \param[in] indices the point cloud indices that need to be used
     * \param[out] centroid the output centroid
-    * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input indices.
     * \note if return value is 0, the centroid is not changed, thus not valid.
-    * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices.
+    * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -171,11 +171,11 @@ namespace pcl
     * The result is returned as a Eigen::Matrix3f.
     * Note: the covariance matrix is not normalized with the number of
     * points. For a normalized covariance, please use
-    * computeNormalizedCovarianceMatrix.
+    * computeCovarianceMatrixNormalized.
     * \param[in] cloud the input point cloud
     * \param[in] centroid the centroid of the set of points in the cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
+    * \return number of valid points used to determine the covariance matrix.
     * In case of dense point clouds, this is the same as the size of input cloud.
     * \note if return value is 0, the covariance matrix is not changed, thus not valid.
     * \ingroup common
@@ -210,7 +210,7 @@ namespace pcl
     * \param[in] cloud the input point cloud
     * \param[in] centroid the centroid of the set of points in the cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
+    * \return number of valid points used to determine the covariance matrix.
     * In case of dense point clouds, this is the same as the size of input cloud.
     * \ingroup common
     */
@@ -239,13 +239,13 @@ namespace pcl
     * The result is returned as a Eigen::Matrix3f.
     * Note: the covariance matrix is not normalized with the number of
     * points. For a normalized covariance, please use
-    * computeNormalizedCovarianceMatrix.
+    * computeCovarianceMatrixNormalized.
     * \param[in] cloud the input point cloud
     * \param[in] indices the point cloud indices that need to be used
     * \param[in] centroid the centroid of the set of points in the cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -276,13 +276,13 @@ namespace pcl
     * The result is returned as a Eigen::Matrix3f.
     * Note: the covariance matrix is not normalized with the number of
     * points. For a normalized covariance, please use
-    * computeNormalizedCovarianceMatrix.
+    * computeCovarianceMatrixNormalized.
     * \param[in] cloud the input point cloud
     * \param[in] indices the point cloud indices that need to be used
     * \param[in] centroid the centroid of the set of points in the cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -320,8 +320,8 @@ namespace pcl
     * \param[in] indices the point cloud indices that need to be used
     * \param[in] centroid the centroid of the set of points in the cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -358,8 +358,8 @@ namespace pcl
     * \param[in] indices the point cloud indices that need to be used
     * \param[in] centroid the centroid of the set of points in the cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -387,14 +387,14 @@ namespace pcl
   }
 
   /** \brief Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop.
-    * Normalized means that every entry has been divided by the number of entries in indices.
+    * Normalized means that every entry has been divided by the number of valid entries in the point cloud.
     * For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix
     * with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
     * \note This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.
     * \param[in] cloud the input point cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
     * \param[out] centroid the centroid of the set of points in the cloud
-    * \return number of valid point used to determine the covariance matrix.
+    * \return number of valid points used to determine the covariance matrix.
     * In case of dense point clouds, this is the same as the size of input cloud.
     * \ingroup common
     */
@@ -428,8 +428,8 @@ namespace pcl
     * \param[in] indices subset of points given by their indices
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
     * \param[out] centroid the centroid of the set of points in the cloud
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -465,8 +465,8 @@ namespace pcl
     * \param[in] indices subset of points given by their indices
     * \param[out] centroid the centroid of the set of points in the cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -494,13 +494,13 @@ namespace pcl
   }
 
   /** \brief Compute the normalized 3x3 covariance matrix for a already demeaned point cloud.
-    * Normalized means that every entry has been divided by the number of entries in indices.
+    * Normalized means that every entry has been divided by the number of entries in the input point cloud.
     * For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix
     * with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
     * \note This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.
     * \param[in] cloud the input point cloud
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
+    * \return number of valid points used to determine the covariance matrix.
     * In case of dense point clouds, this is the same as the size of input cloud.
     * \ingroup common
     */
@@ -530,8 +530,8 @@ namespace pcl
     * \param[in] cloud the input point cloud
     * \param[in] indices subset of points given by their indices
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int
@@ -563,8 +563,8 @@ namespace pcl
     * \param[in] cloud the input point cloud
     * \param[in] indices subset of points given by their indices
     * \param[out] covariance_matrix the resultant 3x3 covariance matrix
-    * \return number of valid point used to determine the covariance matrix.
-    * In case of dense point clouds, this is the same as the size of input cloud.
+    * \return number of valid points used to determine the covariance matrix.
+    * In case of dense point clouds, this is the same as the size of input indices.
     * \ingroup common
     */
   template <typename PointT, typename Scalar> inline unsigned int

common/include/pcl/common/intensity.h

@@ -44,7 +44,7 @@ namespace pcl
 {
   namespace common
   {
-    /** \brief Intensity field accessor provides access to the inetnsity filed of a PoinT
+    /** \brief Intensity field accessor provides access to the intensity filed of a PoinT
       * implementation for specific types should be done in \file pcl/common/impl/intensity.hpp
       */
     template<typename PointT>
@@ -78,7 +78,7 @@ namespace pcl
         p.intensity = intensity;
       }
       /** \brief subtract value from intensity field
-        * \param p point for which to modify inetnsity
+        * \param p point for which to modify intensity
         * \param[in] value value to be subtracted from point intensity
         */
       inline void
@@ -87,7 +87,7 @@ namespace pcl
         p.intensity -= value;
       }
       /** \brief add value to intensity field
-        * \param p point for which to modify inetnsity
+        * \param p point for which to modify intensity
         * \param[in] value value to be added to point intensity
         */
       inline void

common/include/pcl/common/utils.h

@@ -45,7 +45,7 @@ namespace pcl
 {
   namespace utils
   {
-    /** \brief Check if val1 and val2 are equals to an epsilon extent
+    /** \brief Check if val1 and val2 are equal to an epsilon extent
       * \param[in] val1 first number to check
       * \param[in] val2 second number to check
       * \param[in] eps epsilon

common/include/pcl/point_types_conversion.h

@@ -135,8 +135,8 @@ namespace pcl
     if (out.h < 0.f) out.h += 360.f;
   }
 
-  /** \brief Convert a XYZRGB point type to a XYZHSV
-    * \param[in] in the input XYZRGB point
+  /** \brief Convert a XYZRGBA point type to a XYZHSV
+    * \param[in] in the input XYZRGBA point
     * \param[out] out the output XYZHSV point
     * \todo include the A parameter but how?
     */
@@ -241,7 +241,7 @@ namespace pcl
     }
   }
 
-  /** \brief Convert a RGB point cloud to a Intensity
+  /** \brief Convert a RGB point cloud to an Intensity
     * \param[in] in the input RGB point cloud
     * \param[out] out the output Intensity point cloud
     */
@@ -259,7 +259,7 @@ namespace pcl
     }
   }
 
-  /** \brief Convert a RGB point cloud to a Intensity
+  /** \brief Convert a RGB point cloud to an Intensity
     * \param[in] in the input RGB point cloud
     * \param[out] out the output Intensity point cloud
     */
@@ -277,7 +277,7 @@ namespace pcl
     }
   }
 
-  /** \brief Convert a RGB point cloud to a Intensity
+  /** \brief Convert a RGB point cloud to an Intensity
     * \param[in] in the input RGB point cloud
     * \param[out] out the output Intensity point cloud
     */

doc/tutorials/content/adding_custom_ptype.rst

@@ -375,7 +375,7 @@ addition, the type that you want, might already be defined for you.
 * `PointWithRange` - float x, y, z (union with float point[4]), range;
 
   Similar to `PointXYZI`, except `range` contains a measure of the distance
-  from the acqusition viewpoint to the point in the world.
+  from the acquisition viewpoint to the point in the world.
 
 .. code-block:: cpp
 

doc/tutorials/content/basic_structures.rst

@@ -20,7 +20,7 @@ PointCloud is a C++ class which contains the following data fields:
        that resemble an organized image (or matrix) like structure, where the
        data is split into rows and columns. Examples of such point clouds
        include data coming from stereo cameras or Time Of Flight cameras. The
-       advantages of a organized dataset is that by knowing the relationship
+       advantages of an organized dataset is that by knowing the relationship
        between adjacent points (e.g. pixels), nearest neighbor operations are
        much more efficient, thus speeding up the computation and lowering the
        costs of certain algorithms in PCL.
@@ -45,7 +45,7 @@ PointCloud is a C++ class which contains the following data fields:
 
       Example::
 
-        cloud.width = 640; // Image-like organized structure, with 640 rows and 480 columns,
+        cloud.width = 640; // Image-like organized structure, with 480 rows and 640 columns,
         cloud.height = 480; // thus 640*480=307200 points total in the dataset
 
       Example::

doc/tutorials/content/building_pcl.rst

@@ -116,11 +116,11 @@ YYY then XXX will be built but won't appear in the cache.
 
 You can also change the build type:
 
-* **Debug**: means that no optimization is done and all the debugging symbols are imbedded into the libraries file. This is plateform and compiler dependent. On Linux with gcc this is equivalent to running gcc with `-O0 -g -ggdb -Wall`
+* **Debug**: means that no optimization is done and all the debugging symbols are embedded into the libraries file. This is platform and compiler dependent. On Linux with gcc this is equivalent to running gcc with `-O0 -g -ggdb -Wall`
 
-* **Release**: the compiled code is optimized and no debug information will be print out. This will lead to `-O3` for gcc and `-O5` for clang
+* **Release**: the compiled code is optimized and no debug information will be printed out. This will lead to `-O3` for gcc and `-O5` for clang
 
-* **RelWithDebInfo**: the compiled code is optimized but debugging data is also imbedded in the libraries. This is a tradeoff between the two former ones.
+* **RelWithDebInfo**: the compiled code is optimized but debugging data is also embedded in the libraries. This is a tradeoff between the two former ones.
 
 * **MinSizeRel**: this, normally, results in the smallest libraries you can build. This is interesting when building for Android or a restricted memory/space system.
 

doc/tutorials/content/compiling_pcl_dependencies_windows.rst

@@ -177,7 +177,7 @@ like::
         Where to build binaries: C:/PCL_dependencies/flann-1.7.1-src/build
 
     Hit the "Configure" button. Proceed and be sure to choose the correct "Generator" on the next window. 
-    You can safley ignore any warning message about hdf5.    
+    You can safely ignore any warning message about hdf5.    
 
     Now, on my machine I had to manually set the `BUILD_PYTHON_BINDINGS`
     and `BUILD_MATLAB_BINDINGS` to OFF otherwise it would not continue to the next

doc/tutorials/content/compiling_pcl_posix.rst

@@ -46,7 +46,7 @@ And install the result::
 
   make -j2 install
 
-Or alternatively, if you did not change the  variable which declares where PCL should be installed, do::
+Or alternatively, if you did not change the variable which declares where PCL should be installed, do::
 
   sudo make -j2 install
 

doc/tutorials/content/compiling_pcl_windows.rst

@@ -162,7 +162,7 @@ Let's check whether CMake did actually find the needed third party dependencies
 
 - **Eigen** : 
 
-	Eigen is a header-only library, thus, we need only **EIGEN_INCLUDE_DIR** to be set. Hopefully, CMake did fing Eigen.
+	Eigen is a header-only library, thus, we need only **EIGEN_INCLUDE_DIR** to be set. Hopefully, CMake did find Eigen.
 
 	.. image:: images/windows/cmake_eigen_include_dir.png
 		:alt: Eigen include dir
@@ -278,7 +278,7 @@ Building the "ALL_BUILD" project will build everything.
 Installing PCL
 --------------
 
-To install the built libraries and executbles, you need to build the "INSTALL" project in the solution explorer. 
+To install the built libraries and executables, you need to build the "INSTALL" project in the solution explorer. 
 This utility project will copy PCL headers, libraries and executable to the directory defined by the **CMAKE_INSTALL_PREFIX** 
 CMake variable.