Fix several documentation typos

doc/tutorials/content/interactive_icp.rst

@@ -29,7 +29,7 @@ Subdivide the original mesh to make it more dense :
 .. image:: images/interactive_icp/add_sub.png
   :height: 500
 
-Configure the subdivision to 2 or 3 for example : dont forget to apply the modifier
+Configure the subdivision to 2 or 3 for example : don't forget to apply the modifier
 
 .. image:: images/interactive_icp/sub2.png
   :height: 203
@@ -73,7 +73,7 @@ The bool will help us know when the user asks for the next iteration of ICP
    :language: cpp
    :lines: 14-24
 
-This functions takes the reference of a 4x4 matrix and prints the rigid transformation in an human 
+This function takes the reference of a 4x4 matrix and prints the rigid transformation in an human
 readable way.
 
 .. literalinclude:: sources/interactive_icp/interactive_icp.cpp
@@ -182,7 +182,7 @@ and we set max iterations to 1 in lines 90-93.
 As before we check if ICP as converged, if not we exit the program.
 **printf("\033[11A");** is a little trick to go up 11 lines in the terminal to write
 over the last matrix displayed. In short it allows to replace text instead of writing
-new lines; making the ouptut more readable.
+new lines; making the output more readable.
 We increment **iterations** to update the text value in the visualizer.
 
 Now we want to display the rigid transformation from the original transformed point cloud to

doc/tutorials/content/normal_estimation.rst

@@ -144,7 +144,7 @@ representation. To better illustrate this issue, the figure below presents the
 effects of selecting a smaller scale (i.e., small **r** or **k**) versus a
 larger scale (i.e., large **r** or **k**). The left part of the figures depicts
 a reasonable well chosen scale factor, with estimated surface normals
-approximatively perpendicular for the two planar surfaces and small edges
+approximately perpendicular for the two planar surfaces and small edges
 visible all across the table. If the scale factor however is too big (right
 part), and thus the set of neighbors is larger covering points from adjacent
 surfaces, the estimated point feature representations get distorted, with

doc/tutorials/content/octree.rst

@@ -30,7 +30,7 @@ We fist define and instantiate a shared PointCloud structure and fill it with ra
 
 
 Then we create an octree instance which is initialized with its resolution. This octree keeps a vector of point indices within its leaf nodes.
-The resolution parameter describes the length of the smalles voxels at lowest octree level. The depth of the octree is therefore a function of the resolution as well as 
+The resolution parameter describes the length of the smallest voxels at lowest octree level. The depth of the octree is therefore a function of the resolution as well as 
 the spatial dimension of the pointcloud. If a bounding box of the pointcloud is know, it should be assigned to the octree by using the defineBoundingBox method. 
 Then we assign a pointer to the PointCloud and add all points to the octree.
 

doc/tutorials/content/using_pcl_pcl_config.rst

@@ -103,7 +103,7 @@ The executable we are building makes call to PCL functions. So far, we
 have only included the PCL headers so the compilers knows about the
 methods we are calling. We need also to make the linker knows about
 the libraries we are linking against. As said before the, PCL
-found libraries are refered to using ``PCL_LIBRARIES`` variable, all
+found libraries are referred to using ``PCL_LIBRARIES`` variable, all
 that remains is to trigger the link operation which we do calling
 ``target_link_libraries()`` macro.
 PCLConfig.cmake uses a CMake special feature named `EXPORT` which

doc/tutorials/content/vfh_estimation.rst

@@ -112,7 +112,7 @@ Visualizing VFH signatures
 --------------------------
 
 *libpcl_visualization* contains a special **PCLHistogramVisualization** class,
-which is also used by **pcl_viewer** to automaticall display the VFH
+which is also used by **pcl_viewer** to automatically display the VFH
 descriptors as a histogram of float values. For more information, please see
 http://www.pointclouds.org/documentation/overview/visualization.php.
 

doc/tutorials/content/vfh_recognition.rst

@@ -75,7 +75,7 @@ below:
 .. image:: images/vfh_recognition/scene_segmented.jpg
 
 
-Since we're only trying to cover the explicity training/testing of VFH
+Since we're only trying to cover the explicit training/testing of VFH
 signatures in this tutorial, we provide a set of datasets already collected at:
 `vfh_recognition_tutorial_data.tbz
 <https://raw.github.com/PointCloudLibrary/data/master/tutorials/vfh_recognition/vfh_recognition_tutorial_data.tbz>`_.