Voxel Point Sampling function. This function sample N-dimensional points with voxel grid. Points in a same voxel grid will be merged by mean operation for sampling.
@@ -190,7 +190,7 @@ you want to obtain a specified number of points from point cloud.Farthest point sampling function This function sample N-dimensional points with the farthest point policy.
Poisson disk sampling function This function sample N-dimensional points randomly until the number of points keeping minimum distance between selected points.
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Started","How To Contribute","Welcome to PythonRobotics\u2019s documentation!","Aerial Navigation","Drone 3d trajectory following","Rocket powered landing","KF Basics - Part I","KF Basics - Part 2","Appendix","Arm Navigation","N joint arm to point control","Arm navigation with obstacle avoidance","Two joint arm to point control","Bipedal","Bipedal Planner","Control","Inverted Pendulum Control","Move to a Pose Control","Introduction","Ensamble Kalman Filter Localization","Extended Kalman Filter Localization","Histogram filter localization","Localization","Particle filter localization","Unscented Kalman Filter localization","Object shape recognition using circle fitting","Gaussian grid map","k-means object clustering","Lidar to grid map","Mapping","Normal vector estimation","Point cloud Sampling","Ray casting grid map","Object shape recognition using rectangle fitting","Bezier path planning","B-Spline planning","Bug planner","Clothoid path planning","Coverage path planner","Cubic spline planning","Dubins path planning","Dynamic Window Approach","Eta^3 Spline path planning","Optimal Trajectory in a Frenet Frame","Grid based search","Hybrid a star","LQR based path planning","Model Predictive Trajectory Generator","Path Planning","Probabilistic Road-Map (PRM) planning","Quintic polynomials planning","Reeds Shepp planning","Rapidly-Exploring Random Trees (RRT)","State Lattice Planning","Visibility Road-Map planner","Voronoi Road-Map planning","Nonlinear Model Predictive Control with C-GMRES","Linear\u2013quadratic regulator (LQR) speed and steering control","Linear\u2013quadratic regulator (LQR) steering control","Model predictive speed and steering control","Path Tracking","Pure pursuit tracking","Rear wheel feedback control","Stanley control","FastSLAM1.0","FastSLAM 2.0","EKF SLAM","Graph based SLAM","Iterative Closest Point (ICP) Matching","SLAM","Plotting 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Started","How To Contribute","Welcome to PythonRobotics\u2019s documentation!","Aerial Navigation","Drone 3d trajectory following","Rocket powered landing","KF Basics - Part I","KF Basics - Part 2","Appendix","Arm Navigation","N joint arm to point control","Arm navigation with obstacle avoidance","Two joint arm to point control","Bipedal","Bipedal Planner","Control","Inverted Pendulum Control","Move to a Pose Control","Introduction","Ensamble Kalman Filter Localization","Extended Kalman Filter Localization","Histogram filter localization","Localization","Particle filter localization","Unscented Kalman Filter localization","Object shape recognition using circle fitting","Gaussian grid map","k-means object clustering","Lidar to grid map","Mapping","Normal vector estimation","Point cloud Sampling","Ray casting grid map","Object shape recognition using rectangle fitting","Bezier path planning","B-Spline planning","Bug planner","Clothoid path planning","Coverage path planner","Cubic spline planning","Dubins path planning","Dynamic Window Approach","Eta^3 Spline path planning","Optimal Trajectory in a Frenet Frame","Grid based search","Hybrid a star","LQR based path planning","Model Predictive Trajectory Generator","Path Planning","Probabilistic Road-Map (PRM) planning","Quintic polynomials planning","Reeds Shepp planning","Rapidly-Exploring Random Trees (RRT)","State Lattice Planning","Visibility Road-Map planner","Voronoi Road-Map planning","Nonlinear Model Predictive Control with C-GMRES","Linear\u2013quadratic regulator (LQR) speed and steering control","Linear\u2013quadratic regulator (LQR) steering control","Model predictive speed and steering control","Path Tracking","Pure pursuit tracking","Rear wheel feedback control","Stanley control","FastSLAM1.0","FastSLAM 2.0","EKF SLAM","Graph based SLAM","Iterative Closest Point (ICP) Matching","SLAM","Plotting Utilities","Utilities"],titleterms:{"0":[64,65],"1":[1,33,39,64,66],"1d":39,"1st":7,"2":[1,7,33,39,64,65,66,67],"2d":39,"2nd":7,"3":[1,33,39,42,64],"3d":[4,30],"4":[1,39],"5":[1,39],"class":17,"do":6,"function":[17,37],"new":1,A:44,Of:18,The:67,To:1,a_j:39,about:1,ad:1,adapt:33,add:1,advantag:6,aerial:3,algorithm:[1,7,17,18,21,44,54,64,66,67],an:1,anim:1,api:[30,31,33,35,39,40],appendix:8,applic:18,approach:41,approxim:35,area:33,arm:[9,10,11,12],avoid:11,b:35,b_j:39,base:[1,38,44,46,54,67],basic:[6,7,35,52],batch:52,bay:7,belief:6,bezier:34,bias:53,bidirect:44,biped:[13,14],breadth:44,bspline:35,bug:36,c:56,c_j:39,calcul:[23,30,37,39],cast:32,central:6,choos:1,circl:25,close:[33,52],closest:68,clothoid:37,cloud:31,cluster:27,code:1,condit:7,connect:54,consensu:30,constraint:39,construct:37,constructor:17,content:[2,3,8,9,13,15,22,29,48,60,69,71],continu:39,contribut:1,control:[10,12,15,16,17,56,57,58,59,62,63],correl:6,covari:[6,23],coverag:38,criteria:33,cubic:39,curv:39,curvatur:39,d:44,d_j:39,dataset:67,defect:1,definit:18,depend:7,depth:44,deriv:39,describ:6,design:20,dijkstra:[44,54],dimension:[50,67],disk:31,distribut:6,document:[1,2],doe:17,drone:4,dubin:[40,52],dynam:41,ekf:66,ensambl:19,equat:5,estim:[21,30],eta:42,exampl:[1,7,67],exist:1,expect:6,explor:52,extend:20,farthest:31,fastslam1:64,fastslam:65,feedback:62,field:44,filter:[7,19,20,21,23,24],first:[39,44],fit:[25,33],fix:1,follow:4,formul:[56,67],frame:43,frenet:43,fresnel:37,from:[21,23],g:37,gain:7,gaussian:[6,26],gener:[5,7,47,54],get:0,gmre:56,graph:[54,67],grid:[26,28,32,38,44],histogram:21,histori:18,holonom:17,how:[0,1,17,23,39],hybrid:45,i:6,icp:68,implement:1,independ:7,indic:2,inform:52,initi:21,integr:37,interpol:35,introduct:[6,18,64,66,67],invers:12,invert:16,iter:68,joint:[10,12],k:27,kalman:[7,19,20,24],kf:[6,7],kinemat:12,land:5,lane:53,lattic:53,law:7,learn:18,lidar:28,limit:6,linear:[57,58,59],link:12,lite:44,local:[7,19,20,21,22,23,24],lookup:47,loop:52,lqr:[16,46,52,57,58],map:[26,28,29,32,49,54,55],match:68,mathemat:56,matplotlib:1,matrix:23,mean:27,member:17,minim:[33,67],miss:1,model:[16,20,47,56,59],motion:[20,21,50],move:17,mpc:[16,59],multimod:6,multivari:7,n:10,navig:[3,9,11],need:6,node:54,non:17,nonlinear:56,normal:30,object:[25,27,33],observ:[20,21,66],obstacl:[11,54],one:50,oppos:6,optim:[43,47,67],paramet:[37,39],part:[6,7],particl:23,path:[34,37,38,40,42,46,47,48,52,54,60],pathfindercontrol:17,pdf:6,pendulum:16,plan:[34,35,37,39,40,42,46,48,49,50,51,53,55],planar:[12,67],planner:[14,36,38,54],plot:70,plot_curvatur:70,point:[10,12,31,39,68],poisson:31,polar:53,polygon:54,polynomi:50,pose:[17,67],posit:[17,21],potenti:44,power:5,predict:[21,47,56,59,64,66],prm:49,probabilist:[7,49],probabl:21,problem:67,project:1,properti:6,pull:1,pure:61,pursuit:61,python:18,pythonrobot:2,quadrat:[57,58],quintic:50,rai:32,randam:30,random:[6,52],rang:33,ransac:30,rapidli:52,real:67,rear:62,recognit:[25,33],rectangl:33,reed:[51,52],ref:[20,52,56],refer:[5,6,7,17,21,23,24,33,35,37,39,40,50,54,57,58,59,61,62,63,64,65,66,67,68],regul:[57,58],report:1,repres:6,represent:67,request:1,requir:0,resampl:64,review:1,road:[49,54,55],robot:[12,17,18,50],rocket:5,rrt:52,rule:7,s:[2,6,17],sampl:[30,31,47,53],se:67,search:[33,44,54],second:39,segment:33,shape:[25,33],sheep:52,shepp:51,shortest:54,simul:[5,52,64,66],slam:[66,67,69],softwar:18,solv:37,span:38,speed:[57,59],spiral:38,spline:[35,39,42],sponsor:1,stanlei:63,star:45,start:0,state:53,steer:[57,58,59],step1:[21,33,37,39,54],step2:[21,33,37,39,54],step3:[21,37,39,54],step4:[21,39],step:[1,66],structur:7,submit:1,support:1,sweep:38,system:17,tabl:[2,47],tangent:39,term:6,termin:39,theorem:6,thi:[0,1],through:[64,66],track:[60,61],trajectori:[4,43,47],tree:[38,52],triangl:30,two:[12,50],uniform:53,unimod:6,unittest:1,univari:7,unknown:39,unscent:24,updat:[21,64,66],us:[0,25,33,54],util:[70,71],variabl:6,varianc:[6,33],vector:[30,39],vehicl:59,visibl:54,voronoi:55,voxel:31,walk:[64,66],wavefront:38,we:6,welcom:2,what:[0,6],wheel:62,why:6,window:41,work:17,world:67,write:1,x:50,y:50}}) 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