Merge remote-tracking branch 'origin/master' into issue-2507

doc/quickstart.rst

@@ -38,9 +38,9 @@ Install Conda
 Conda installation instructions can be found at the following links. Read
 through them a bit for your platform so you have an idea what to expect.
 
-* `Windows <https://conda.io/docs/user-guide/install/windows.html>`__
-* `macOS <https://conda.io/docs/user-guide/install/macos.html>`__
-* `Linux <https://conda.io/docs/user-guide/install/linux.html>`__
+* `Windows <https://conda.io/projects/conda/en/latest/user-guide/install/windows.html>`__
+* `macOS <https://conda.io/projects/conda/en/latest/user-guide/install/macos.html>`__
+* `Linux <https://conda.io/projects/conda/en/latest/user-guide/install/linux.html>`__
 
 .. note::
 
@@ -56,7 +56,7 @@ On macOS and Linux, all Conda commands are typed into a terminal window. On
 Windows, commands are typed into the Anaconda Prompt window. Instructions can
 be found in the Conda `Getting Started`_ guide.
 
-.. _`Getting Started`: https://conda.io/docs/user-guide/getting-started.html#starting-conda
+.. _`Getting Started`: https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#starting-conda
 
 
 Test Installation
@@ -79,7 +79,7 @@ the PDAL maintenance branch.
     It is a good idea to install PDAL in it's own environment (or
     add it to an existing one). You will **NOT** want to add it to your default
     environment named ``base``. Managing environments is beyond the scope of
-    the quickstart, but you can read more about it `here <https://conda.io/docs/user-guide/getting-started.html#managing-envs>`_.
+    the quickstart, but you can read more about it `here <https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-envs>`_.
 
 To install the PDAL package so that we can use it to run PDAL commands, we run
 the following command to create an environment named ``myenv``, installing PDAL

doc/references.rst

@@ -52,8 +52,6 @@ Reference
 
 .. [Mesh2009] ALoopingIcon. "Meshing Point Clouds." *MESHLAB STUFF*. n.p., 7 Sept. 2009. Web. 13 Nov. 2015.
 
-.. [Mongus2012] Mongus, D., Zalik, B., 2012. Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS J. Photogramm. Remote Sens. 67, 1–12.
-
 .. [Pingel2013] Pingel, Thomas J., Keith C. Clarke, and William A. McBride. “An Improved Simple Morphological Filter for the Terrain Classification of Airborne LIDAR Data.” ISPRS Journal of Photogrammetry and Remote Sensing 77 (2013): 21–30.
 
 .. [Rusu2008] Rusu, Radu Bogdan, et al. "Towards 3D point cloud based object maps for household environments." Robotics and Autonomous Systems 56.11 (2008): 927-941.

doc/stages/filters.mongoexpression.rst → doc/stages/filters.mongo.rst

@@ -1,9 +1,9 @@
-.. _filters.mongoexpression:
+.. _filters.mongo:
 
-filters.mongoexpression
+filters.mongo
 ========================
 
-The **Mongo Expression Filter** applies query logic to the input
+The **Mongo Filter** applies query logic to the input
 point cloud based on a MongoDB-style query expression using the
 point cloud attributes.
 
@@ -21,7 +21,7 @@ This example passes through only the points whose Classification is non-zero.
     [
         "input.las",
         {
-            "type": "filters.expression",
+            "type": "filters.mongo",
             "expression": {
                 "Classification": { "$ne": 0 }
             }
@@ -38,7 +38,7 @@ is greater than 1.
     [
         "input.las",
         {
-            "type": "filters.expression",
+            "type": "filters.mongo",
             "expression": { "$and": [
                 { "ReturnNumber": "NumberOfReturns" },
                 { "NumberOfReturns": { "$gt": 1 } }

doc/stages/filters.rst

@@ -36,16 +36,15 @@ invalidate an existing KD-tree.
    filters.cluster
    filters.colorinterp
    filters.colorization
+   filters.covariancefeatures
    filters.dem
    filters.eigenvalues
    filters.estimaterank
    filters.elm
    filters.ferry
    filters.hag
    filters.info
-   filters.kdistance
    filters.lof
-   filters.mongus
    filters.neighborclassifier
    filters.nndistance
    filters.normal
@@ -74,6 +73,10 @@ invalidate an existing KD-tree.
 :ref:`filters.colorization`
     Fetch and assign RGB color information from a GDAL-readable datasource.
 
+:ref:`filters.covariancefeatures`
+    Filter that calculates local features based on the covariance matrix of a
+    point's neighborhood.
+
 :ref:`filters.eigenvalues`
     Compute pointwise eigenvalues, based on k-nearest neighbors.
 
@@ -90,17 +93,10 @@ invalidate an existing KD-tree.
     Compute pointwise height above ground estimate. Requires points to be
     classified as ground/non-ground prior to estimating.
 
-:ref:`filters.kdistance`
-    Compute pointwise K-Distance (the Euclidean distance to a point's k-th
-    nearest neighbor). [Deprecated - use :ref:`filters.nndistance`]
-
 :ref:`filters.lof`
     Compute pointwise Local Outlier Factor (along with K-Distance and Local
     Reachability Distance).
 
-:ref:`filters.mongus`
-    Label ground/non-ground returns using [Mongus2012]_.
-
 :ref:`filters.neighborclassifier`
     Update pointwise classification using k-nearest neighbor consensus voting.
 
@@ -196,7 +192,7 @@ the input. These filters will invalidate an existing KD-tree.
    filters.iqr
    filters.locate
    filters.mad
-   filters.mongoexpression
+   filters.mongo
    filters.range
    filters.sample
    filters.tail
@@ -227,7 +223,7 @@ the input. These filters will invalidate an existing KD-tree.
     Cull points falling outside the computed Median Absolute Deviation for a
     given dimension.
 
-:ref:`filters.mongoexpression`
+:ref:`filters.mongo`
     Cull points using MongoDB-style expression syntax.
 
 :ref:`filters.range`

doc/stages/readers.e57.rst

@@ -0,0 +1,77 @@
+.. _readers.e57:
+
+readers.e57
+===========
+
+The **E57 Reader** supports reading from E57 files.
+
+The reader supports E57 files with Cartesian point clouds.
+
+.. note::
+
+   E57 files can contain multiple point clouds stored in a single
+   file.  If that is the case, the reader will read all the points
+   from all of the internal point clouds as one.
+
+   Only dimensions present in all of the point clouds will be read.
+
+.. note::
+
+   Point clouds stored in spherical format are not supported.
+
+.. note::
+
+   The E57 `cartesianInvalidState` dimension is mapped to the Omit
+   PDAL dimension.  A range filter can be used to filter out the
+   invalid points.
+
+.. plugin::
+
+.. streamable::
+
+
+Example 1
+---------
+
+.. code-block:: json
+
+  [
+      {
+          "type":"readers.e57",
+          "filename":"inputfile.e57"
+      },
+      {
+          "type":"writers.text",
+          "filename":"outputfile.txt"
+      }
+  ]
+
+
+Example 2
+---------
+
+.. code-block:: json
+
+  [
+      {
+          "type":"readers.e57",
+          "filename":"inputfile.e57"
+      },
+      {
+          "type":"filters.range",
+          "limits":"Omit[0:0]"
+      },
+      {
+          "type":"writers.text",
+          "filename":"outputfile.txt"
+      }
+  ]
+
+  
+Options
+-------
+
+_`filename`
+  E57 file to read [Required]
+
+.. include:: reader_opts.rst

doc/stages/readers.ept.rst

@@ -98,7 +98,7 @@ threads
     Number of worker threads used to download and process EPT data.  A
     minimum of 4 will be used no matter what value is specified.
 
-.. _Entwine Point Tile: https://github.com/connormanning/entwine/blob/master/doc/entwine-point-tile.md
+.. _Entwine Point Tile: https://entwine.io/entwine-point-tile.html
 .. _Entwine: https://entwine.io/
 .. _Potree: http://potree.entwine.io/data/nyc.html
 .. _Plasio: http://speck.ly/?s=http%3A%2F%2Fc%5B0-7%5D.greyhound.io&r=ept%3A%2F%2Fna.entwine.io%2Fnyc&ca=-0&ce=49.06&ct=-8239196%2C4958509.308%2C337&cd=42640.943&cmd=125978.13&ps=2&pa=0.1&ze=1&c0s=remote%3A%2F%2Fimagery%3Furl%3Dhttp%3A%2F%2Fserver.arcgisonline.com%2FArcGIS%2Frest%2Fservices%2FWorld_Imagery%2FMapServer%2Ftile%2F%7B%7Bz%7D%7D%2F%7B%7By%7D%7D%2F%7B%7Bx%7D%7D.jpg

doc/stages/writers.e57.rst

@@ -0,0 +1,55 @@
+.. _writers.e57:
+
+writers.e57
+===========
+
+The **E57 Writer** supports writing to E57 files.
+
+The writer supports E57 files with Cartesian point clouds.
+
+.. note::
+
+   E57 files can contain multiple point clouds stored in a single
+   file.  The writer will only write a single cloud per file.
+
+.. note::
+
+   Spherical format points are not supported.
+
+.. note::
+
+   The E57 `cartesianInvalidState` dimension is mapped to the Omit
+   PDAL dimension.  A range filter can be used to filter out the
+   invalid points.
+
+.. plugin::
+
+.. streamable::
+
+
+Example
+-------
+
+.. code-block:: json
+
+  [
+      {
+          "type":"readers.las",
+          "filename":"inputfile.las"
+      },
+      {
+          "type":"writers.e57",
+          "filename":"outputfile.e57",
+	  "doublePrecision":false
+      }
+  ]
+
+  
+Options
+-------
+
+_`filename`
+  E57 file to write [Required]
+
+doublePrecision
+  Use double precision for storage (false by default).

doc/stages/writers.sbet.rst

@@ -3,7 +3,7 @@
 writers.sbet
 ============
 
-The **SBET writer** writes files in the SBET format, used for exchange data from interital measurement units (IMUs).
+The **SBET writer** writes files in the SBET format, used for exchange data from inertial measurement units (IMUs).
 
 .. embed::