Transcription of PCL Tutorial: - The Point Cloud Library By Example
1 PCL Tutorial: The Point Cloud Library By ExampleJeff DelmericoVision and Perceptual Machines Lab106 Davis HallUB North 11, 2013 Jeff DelmericoFebruary 11, 20131/38 Point CloudsDefinitionA Point Cloud is a data structure used to represent a collection ofmulti-dimensional points and is commonly used to representthree-dimensional a 3D Point Cloud , the points usually represent the X, Y, and Zgeometric coordinates of an underlying sampled surface. Whencolor information is present, the Point Cloud becomes DelmericoFebruary 11, 2013 Introduction2/38 Where do Point clouds come from?IRGB-D camerasIStereo camerasI3D laser scannersITime-of-flight camerasISythetically from software( Blender)Jeff DelmericoFebruary 11, 2013 Introduction3/38 Point Cloud LibraryIPCL is a large scale, open project for 2D/3D image and pointcloud processing (in C++, w/ new python bindings).IThe PCL framework contains numerous state-of-the artalgorithms including filtering, feature estimation, surfacereconstruction, registration, model fitting and is cross-platform, and has been successfully compiled anddeployed on Linux, MacOS, Windows, and DelmericoFebruary 11, 2013 Introduction4/38 Getting PCLIF irst, download PCL for your system from: you want to try the python bindings (currently for only asubset of the full PCL functionality), go here: provides the 3D processing pipeline for ROS, so you canalso get the perceptionpcl stack and still use PCL depends on Boost, Eigen, FLANN, and DelmericoFebruary 11, 2013 Using PCL5/38 Basic StructuresThe basic data type in PCL is a PointCloud.
2 A PointCloud is atemplated C++ class which contains the following data fields:Iwidth (int)- secifies the width of the Point Cloud dataset inthe number of total number of points in the Cloud (equal with thenumber of elements in points) for unorganized datasetsIthe width (total number of points in a row) of an organizedpoint Cloud datasetIheight (int)- Specifies the height of the Point Cloud datasetin the number of to1for unorganized Point cloudsIthe height (total number of rows) of an organized Point clouddatasetIpoints (std::vector PointT )- Contains the data arraywhere all the points of type PointT are DelmericoFebruary 11, 2013 Using PCL6/38 Basic StructuresIisdense (bool)- Specifies if all the data inpointsis finite(true), or whether the XYZ values of certain points mightcontain Inf/NaN values (false).Isensororigin(Eigen::Vector4f)- Specifies the sensoracquisition pose (origin/translation). This member is usuallyoptional, and not used by the majority of the algorithms (Eigen::Quaternionf)- Specifies thesensor acquisition pose (orientation).
3 This member is usuallyoptional, and not used by the majority of the algorithms DelmericoFebruary 11, 2013 Using PCL7/38 Point TypesIPointXYZ- float x, y, zIPointXYZI- float x, y, z, intensityIPointXYZRGB- float x, y, z, rgbIPointXYZRGBA- float x, y, z, uint32t rgbaINormal- float normal[3], curvatureIPointNormal- float x, y, z, normal[3], curvatureIHistogram- float histogram[N]IAnd many, many, more. Plus you can define new types to suityour DelmericoFebruary 11, 2013 Using PCL8/38 Building PCL ProjectsPCL relies onCMakeas a build tool. CMake just requires thatyou place a file on your (VERSION FATALERROR)project(MYGRANDPROJECT)findpa ckage(PCL REQUIRED COMPONENTS common io)includedirectories($PCLINCLUDEDIRS)li nkdirectories($PCLLIBRARYDIRS)adddefinit ions($PCLDEFINITIONS)addexecutable(pcdwr itetest )targetlinklibraries(pcdwritetest $PCLCOMMONLIBRARIES$PCLIOLIBRARIES)Jeff DelmericoFebruary 11, 2013 Using PCL9/38 Building PCL ProjectsGenerating the Makefile & Building the Project$cd /PATH/TO/MY/GRAND/PROJECT$ mkdir build$ cd build$ cmake.
4 $ makeJeff DelmericoFebruary 11, 2013 Using PCL10/38 PCD File FormatA simple file format for storing multi-dimensional Point data. Itconsists of a text header (with the fields below), followed by thedata in ASCII (w/ points on separate lines) or binary (a memorycopy of thepointsvector of the PC).IVERSION - the PCD file version (usually .7)IFIELDS - the name of each dimension/field that a Point can have ( FIELDSx y z )ISIZE - the size of each dimension in bytes ( a float is 4)ITYPE - the type of each dimension as a char (I= signed,U= unsigned,F=float)ICOUNT - the number of elements in each dimension ( x, y, or z would onlyhave 1, but a histogram would haveN)IWIDTH - the width of the Point cloudIHEIGHT - the height of the Point cloudIVIEWPOINT - an acquisition viewpoint for the points: translation (tx ty tz) +quaternion (qw qx qy qz)IPOINTS - the total number of points in the cloudIDATA - the data type that the Point Cloud data is stored in (asciiorbinary)Jeff DelmericoFebruary 11, 2013I/O11/38 PCD Example #.
5 PCD - Point Cloud Data file formatVERSION .7 FIELDS x y z rgbSIZE 4 4 4 4 TYPE F F F FCOUNT 1 1 1 1 WIDTH 213 HEIGHT 1 VIEWPOINT 0 0 0 1 0 0 0 POINTS 213 DATA 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + DelmericoFebruary 11, 2013I/O12/38 Writing PCD #i n c l u d e<p c l / i o / p c di o . h>#i n c l u d e<p c l / p o i n tt y p e s . h>i n tm a i n (i n ta r g c ,c h a r a r g v ){p c l : : P o i n t C l o u d<p c l : : PointXYZ>c l o u d ;// F i l l in the Cloud datac l o u d . w i d t h= 5 0 ;c l o u d . h e i g h t= 1 ;c l o u d . i sd e n s e =f a l s e;c l o u d . p o i n t s . r e s i z e ( c l o u d . w i d t h c l o u d . h e i g h t ) ;f o r( s i z eti = 0 ; i<c l o u d . p o i n t s . s i z e( ) ; ++i ){c l o u d . p o i n t s [ i ] . x = 1 0 2 4 r a n d ( ) / (RANDMAX + 1 . 0 f ) ;c l o u d . p o i n t s [ i ].}}
6 Y = 1 0 2 4 r a n d ( ) / (RANDMAX + 1 . 0 f ) ;c l o u d . p o i n t s [ i ] . z = 1 0 2 4 r a n d ( ) / (RANDMAX + 1 . 0 f ) ;}p c l : : i o : : s a v e P C D F i l e A S C I I ( t e s tp c d . p c d , c l o u d ) ;r e t u r n( 0 ) ;}Jeff DelmericoFebruary 11, 2013I/O13/38 Reading PCD #i n c l u d e<p c l / i o / p c di o . h>#i n c l u d e<p c l / p o i n tt y p e s . h>i n tm a i n (i n ta r g c ,c h a r a r g v ){p c l : : P o i n t C l o u d<p c l : : PointXYZ>: : P t r c l o u d (newp c l : : P o i n t C l o u d<p c l : : PointXYZ>);// Load the f i l ei f( p c l : : i o : : l o a d P C D F i l e<p c l : : PointXYZ>( t e s tp c d . p c d , c l o u d ) == 1){PCLERROR ( C o u l d n tr e a df i l et e s tp c d . p c d\n ) ;r e t u r n( 1 ) ;}// Do some p r o c e s s i n g on the Cloud herer e t u r n( 0 ) ;}Jeff DelmericoFebruary 11, 2013I/O14/38 Getting Point Clouds from #i n c l u d e<p c l / i o / o p e n n ig r a b b e r.
7 H>#i n c l u d e<p c l / v i s u a l i z a t i o n / c l o u dv i e w e r . h>c l a s sS i m p l e O p e n N I V i e w e r{p u b l i c:S i m p l e O p e n N I V i e w e r ( ) : v i e w e r ( PCLOpenNIV i e w e r ){}v o i dc l o u dc b(c o n s tp c l : : P o i n t C l o u d<p c l : : PointXYZRGBA>: : C o n s t P t r &c l o u d ){i f( ! v i e w e r . w a s S t o p p e d ( ) )v i e w e r . s h o w C l o u d ( c l o u d ) ;}p c l : : v i s u a l i z a t i o n : : C l o u d V i e w e r v i e w e r ;Jeff DelmericoFebruary 11, 2013I/O15/38 Getting Point Clouds from o i dr u n ( ){p c l : : G r a b b e r i n t e r f a c e =newp c l : : O p e n N I G r a b b e r ( ) ;b o o s t : : f u n c t i o n<v o i d(c o n s tp c l : : P o i n t C l o u d<p c l : : PointXYZRGBA>: : C o n s t P t r&)>f =b o o s t : : b i n d (& S i m p l e O p e n N I V i e w e r : : c l o u dc b,t h i s,1 ) ;i n t e r f a c e >r e g i s t e r C a l l b a c k ( f ) ;i n t e r f a c e >s t a r t( ) ;w h i l e( !)}}
8 V i e w e r . w a s S t o p p e d ( ) ){b o o s t : : t h i st h r e a d : : s l e e p ( b o o s t : : p o s i xt i m e : : s e c o n d s ( 1 ) ) ;}i n t e r f a c e >s t o p ( ) ;}};i n tm a i n ( ){S i m p l e O p e n N I V i e w e r v ;v . r u n ( ) ;r e t u r n0 ;}Jeff DelmericoFebruary 11, 2013I/O16/38 Normal o i dd o w n s a m p l e ( p c l : : P o i n t C l o u d<p c l : : PointXYZRGB>: : P t r &p o i n t s ,f l o a tl e a fs i z e ,p c l : : P o i n t C l o u d<p c l : : PointXYZRGB>: : P t r &d o w n s a m p l e do u t ){p c l : : V o x e l G r i d<p c l : : PointXYZRGB>v o xg r i d ;v o xg r i d . s e t L e a f S i z e ( l e a fs i z e , l e a fs i z e ,l e a fs i z e ) ;v o xg r i d . s e t I n p u t C l o u d ( p o i n t s ) ;v o xg r i d . f i l t e r ( d o w n s a m p l e do u t ) ;}v o i dc o m p u t es u r f a c en o r m a l s ( p c l : : P o i n t C l o u d<p c l : : PointXYZRGB>: : P t r &p o i n t s ,f l o a tn o r m a lr a d i u s , p c l : : P o i n t C l o u d<p c l : : Normal>: : P t r &n o r m a l so u t ){p c l : : N o r m a l E s t i m a t i o n<p c l : : PointXYZRGB , p c l : : Normal>n o r me s t ;// Use a FLANN based KdTree to perform neighborhood s e a r c h e sn o r me s t.
9 S e t S e a r c h M e t h o d ( p c l : : s e a r c h : : KdTree<p c l : : PointXYZRGB>: : P t r(newp c l : : s e a r c h : : KdTree<p c l : : PointXYZRGB>) ) ;// S p e c i f y the l o c a l neighborhood s i z e f o r computing the s u r f a c e normalsn o r me s t . s e t R a d i u s S e a r c h ( n o r m a lr a d i u s ) ;// Set the input p o i n t sn o r me s t . s e t I n p u t C l o u d ( p o i n t s ) ;// Estimate the s u r f a c e normals and s t o r e the r e s u l t in normalsout n o r me s t . c o m p u t e ( n o r m a l so u t ) ;}Jeff DelmericoFebruary 11, 20133D Features17 o i dv i s u a l i z en o r m a l s (c o n s tp c l : : P o i n t C l o u d<p c l : : PointXYZRGB>: : P t r p o i n t s ,c o n s tp c l : : P o i n t C l o u d<p c l : : PointXYZRGB>: : P t r n o r m a lp o i n t s ,c o n s tp c l : : P o i n t C l o u d<p c l : : Normal>: : P t r n o r m a l s ){p c l : : v i s u a l i z a t i o n : : P C L V i s u a l i z e r v i z ;v i z.
10 A d d P o i n t C l o u d ( p o i n t s , p o i n t s ) ;v i z . a d d P o i n t C l o u d ( n o r m a lp o i n t s , n o r m a lp o i n t s ) ;v i z . a d d P o i n t C l o u d N o r m a l s<p c l : : PointXYZRGB , p c l : : Normal>( n o r m a lp o i n t s , n o r m a l s , 1 , 0 . 0 1 , n o r m a l s ) ;v i z . s p i n ( ) ;}i n tm a i n (i n ta r g c ,c h a r a r g v ){// Load data from pcd ..p c l : : P o i n t C l o u d<p c l : : PointXYZRGB>: : P t r d s (newp c l : : P o i n t C l o u d<p c l : : PointXYZRGB>);p c l : : P o i n t C l o u d<p c l : : Normal>: : P t r n o r m a l s (newp c l : : P o i n t C l o u d<p c l : : Normal>);// Downsample the cloudc o n s t f l o a tv o x e lg r i dl e a fs i z e = 0 . 0 1 ;d o w n s a m p l e ( c l o u d , v o x e lg r i dl e a fs i z e , d s ) ;// Compute s u r f a c e normalsc o n s t f l o a tn o r m a lr a d i u s = 0.
