diff --git a/_sources/modules/slam/FastSLAM1/FastSLAM1_main.rst.txt b/_sources/modules/slam/FastSLAM1/FastSLAM1_main.rst.txt index f09125df..f19527c4 100644 --- a/_sources/modules/slam/FastSLAM1/FastSLAM1_main.rst.txt +++ b/_sources/modules/slam/FastSLAM1/FastSLAM1_main.rst.txt @@ -39,7 +39,7 @@ an array of landmark locations - The blue line is the true trajectory - The red line is the estimated trajectory - The red dots represent the distribution of particles -- The black line represent dead reckoning tracjectory +- The black line represent dead reckoning trajectory - The blue x is the observed and estimated landmarks - The black x is the true landmark diff --git a/_sources/modules/slam/ekf_slam/ekf_slam_main.rst.txt b/_sources/modules/slam/ekf_slam/ekf_slam_main.rst.txt index 5a23b4c8..70a7d131 100644 --- a/_sources/modules/slam/ekf_slam/ekf_slam_main.rst.txt +++ b/_sources/modules/slam/ekf_slam/ekf_slam_main.rst.txt @@ -151,7 +151,7 @@ values incorporate the error into the estimation. :math:`\begin{equation*} X_{DR} = FX + B(U + R) \end{equation*}` -The implementation of the motion model prediciton code is shown in +The implementation of the motion model prediction code is shown in ``motion_model``. The ``observation`` function shows how the simulation uses (or doesn’t use) the process noise ``Rsim`` to the find the ground truth and dead reckoning estimates of the pose. diff --git a/modules/slam/FastSLAM1/FastSLAM1.html b/modules/slam/FastSLAM1/FastSLAM1.html index 02ba9d70..b48732a8 100644 --- a/modules/slam/FastSLAM1/FastSLAM1.html +++ b/modules/slam/FastSLAM1/FastSLAM1.html @@ -142,7 +142,7 @@ an array of landmark locations

  • The blue line is the true trajectory

  • The red line is the estimated trajectory

  • The red dots represent the distribution of particles

    • -

  • The black line represent dead reckoning tracjectory

    • +

  • The black line represent dead reckoning trajectory

  • The blue x is the observed and estimated landmarks

  • The black x is the true landmark

    • diff --git a/modules/slam/ekf_slam/ekf_slam.html b/modules/slam/ekf_slam/ekf_slam.html index 3bafd740..8abfcdad 100644 --- a/modules/slam/ekf_slam/ekf_slam.html +++ b/modules/slam/ekf_slam/ekf_slam.html @@ -237,7 +237,7 @@ values incorporate the error into the estimation.

    \(\begin{equation*} R= \begin{bmatrix} \sigma_v\\ \sigma_w\\ \end{bmatrix} \end{equation*}\)

    \(\begin{equation*} X_{true} = FX + B(U) \end{equation*}\)

    \(\begin{equation*} X_{DR} = FX + B(U + R) \end{equation*}\)

    -

    The implementation of the motion model prediciton code is shown in +

    The implementation of the motion model prediction code is shown in motion_model. The observation function shows how the simulation uses (or doesn’t use) the process noise Rsim to the find the ground truth and dead reckoning estimates of the pose.

    diff --git a/searchindex.js b/searchindex.js index cdecaae2..94d3a313 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ 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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","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) 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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","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"],titleterms:{"0":[62,63],"1":[1,62,64],"1st":7,"2":[1,7,62,63,64,65],"2nd":7,"3":[1,40,62],"3d":4,"4":1,"5":1,"class":17,"do":6,"function":[17,35],"new":1,A:42,Of:18,The:65,To:1,about:1,ad:1,add:1,advantag:6,aerial:3,algorithm:[1,7,17,18,21,42,52,62,64,65],an:1,anim:1,api:38,appendix:8,applic:18,approach:39,arm:[9,10,11,12],avoid:11,b:33,base:[1,36,42,44,52,65],basic:[6,7,50],batch:50,bay:7,belief:6,bezier:32,bias:51,bidirect:42,biped:[13,14],breadth:42,bug:34,c:54,calcul:[23,35],cast:30,central:6,choos:1,circl:25,close:50,closest:66,clothoid:35,cluster:27,code:1,condit:7,connect:52,construct:35,constructor:17,content:[2,3,8,9,13,15,22,29,46,58,67],contribut:1,control:[10,12,15,16,17,54,55,56,57,60,61],correl:6,covari:[6,23],coverag:36,cubic:37,d:42,dataset:65,defect:1,definit:18,depend:7,depth:42,describ:6,design:20,dijkstra:[42,52],dimension:[48,65],distribut:6,document:[1,2],doe:17,drone:4,dubin:[38,50],dynam:39,ekf:64,ensambl:19,equat:5,estim:21,eta:40,exampl:[1,7,65],exist:1,expect:6,explor:50,extend:20,fastslam1:62,fastslam:63,feedback:60,field:42,filter:[7,19,20,21,23,24],first:42,fit:[25,31],fix:1,follow:4,formul:[54,65],frame:41,frenet:41,fresnel:35,from:[21,23],g:35,gain:7,gaussian:[6,26],gener:[5,7,45,52],get:0,gmre:54,graph:[52,65],grid:[26,28,30,36,42],histogram:21,histori:18,holonom:17,how:[0,1,17,23],hybrid:43,i:6,icp:66,implement:1,independ:7,indic:2,inform:50,initi:21,integr:35,introduct:[6,18,62,64,65],invers:12,invert:16,iter:66,joint:[10,12],k:27,kalman:[7,19,20,24],kf:[6,7],kinemat:12,land:5,lane:51,lattic:51,law:7,learn:18,lidar:28,limit:6,linear:[55,56,57],link:12,lite:42,local:[7,19,20,21,22,23,24],lookup:45,loop:50,lqr:[16,44,50,55,56],map:[26,28,29,30,47,52,53],match:66,mathemat:54,matplotlib:1,matrix:23,mean:27,member:17,minim:65,miss:1,model:[16,20,45,54,57],motion:[20,21,48],move:17,mpc:[16,57],multimod:6,multivari:7,n:10,navig:[3,9,11],need:6,node:52,non:17,nonlinear:54,object:[25,27,31],observ:[20,21,64],obstacl:[11,52],one:48,oppos:6,optim:[41,45,65],paramet:35,part:[6,7],particl:23,path:[32,35,36,38,40,44,45,46,50,52,58],pathfindercontrol:17,pdf:6,pendulum:16,plan:[32,33,35,37,38,40,44,46,47,48,49,51,53],planar:[12,65],planner:[14,34,36,52],point:[10,12,66],polar:51,polygon:52,polynomi:48,pose:[17,65],posit:[17,21],potenti:42,power:5,predict:[21,45,54,57,62,64],prm:47,probabilist:[7,47],probabl:21,problem:65,project:1,properti:6,pull:1,pure:59,pursuit:59,python:18,pythonrobot:2,quadrat:[55,56],quintic:48,rai:30,random:[6,50],rapidli:50,real:65,rear:60,recognit:[25,31],rectangl:31,reed:[49,50],ref:[20,50,54],refer:[5,6,7,17,21,23,24,35,38,48,52,55,56,57,59,60,61,62,63,64,65,66],regul:[55,56],report:1,repres:6,represent:65,request:1,requir:0,resampl:62,review:1,road:[47,52,53],robot:[12,17,18,48],rocket:5,rrt:50,rule:7,s:[2,6,17],sampl:[45,51],se:65,search:[42,52],shape:[25,31],sheep:50,shepp:49,shortest:52,simul:[5,50,62,64],slam:[64,65,67],softwar:18,solv:35,span:36,speed:[55,57],spiral:36,spline:[33,37,40],sponsor:1,stanlei:61,star:43,start:0,state:51,steer:[55,56,57],step1:[21,35,52],step2:[21,35,52],step3:[21,35,52],step4:21,step:[1,64],structur:7,submit:1,support:1,sweep:36,system:17,tabl:[2,45],term:6,theorem:6,thi:[0,1],through:[62,64],track:[58,59],trajectori:[4,41,45],tree:[36,50],two:[12,48],uniform:51,unimod:6,unittest:1,univari:7,unscent:24,updat:[21,62,64],us:[0,25,31,52],variabl:6,varianc:6,vehicl:57,visibl:52,voronoi:53,walk:[62,64],wavefront:36,we:6,welcom:2,what:[0,6],wheel:60,why:6,window:39,work:17,world:65,write:1,x:48,y:48}}) 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