get
PURPOSE 
Get function for Gaussians.
SYNOPSIS
function out=get(G,field)
DESCRIPTION
Get function for Gaussians.
Returns the information associated with a Gaussian
Possible queries are
- dim: The dimensionality of the space where the Gaussian is defined
- mean: The mean vector.
- covariance: The covariance matrix.
- invCovariance: The inverse of the covariance matrix.
- volume: The volume of the ellipsoid defined from the covariance matrix.
- normalizationFactor: The normalization factor for a Gaussian (the constant
that multiplies the exponential defining the Gaussian
distribution).
- ellipse: Returns a line representing the iso-countour of the
ellipse defined from the mean and covariance at a 90% confidence
value. For higher-dimensional Gaussians, we marginalize and only
use the first 2 components of the Gaussians.CROSS-REFERENCE INFORMATION
This function calls:
- size Number of poses in a trajectory.
- size Size of the state estimated in the filter
- size Size (rows/columns) of an interval matrix.
- size Number of parameters of the pose.
- size Number of readings stored in the Sensor.
- Trajectory Trajectory constructor.
- Experiment1a Simulated experiment.
- Experiment1b Simulated experiment.
- Experiment1c Simulated experiment.
- GetCrossCovarianceWithPose Cross covariance of a given pose with the current one.
- GetPoseEstimation Estimation of a given pose.
- P_EKF Constructor for Extended Kalman Filter for PoseSLAM.
- Update Adds a new pose to an EKF filter.
- get Get function for EKF filters.
- EKF_Prediction EKF Prediction step.
- EKF_Update EKF Update step.
- ComputeNEES Quantifies the consitency of a filter.
- GetMarginalCovariance Marginal covariance between the current pose and a previous one.
- GetPoseCovariance Marginal covariance of a given pose.
- GetPoseData Defines a PoseData objects from the information stored in the filter.
- GetPoseMean Mean estimation of a given pose.
- InitFilterPlot Initializes the plot of a filter.
- P_Filter Construtor for the base Pose SLAM filter.
- ProbDistance2PoseBelowThreshold Checks if pose 'step' is close enought to the current pose.
- Simulation Simulates a robot performing Pose SLAM.
- TreeDetectLoops Propose loops based on the distance between poses using a tree for the search.
- UpdateFilterPlot Updates the plot of a filter with a new pose.
- UpdateLoop Adds a loop to the filter.
- get Generic get function for filters.
- GetCrossCovarianceWithPose Cross covariance of a given pose with the current one.
- GetPoseCovariance Covariance estimation of a given pose.
- GetPoseEstimation Estimation of a given pose.
- GetPoseMean Mean estimation of a given pose.
- P_TRO Constructor of TRO Filters for Pose SLAM.
- Update Adds a new pose to a TRO filter.
- UpdateLoop Adds a new loop to a TRO filter.
- get Get function for TRO filters.
- TRO_Prediction Prediction Step.
- TRO_Update Filter update step.
- InsertInTree Inserts an element in a BTree.
- SearchInTree Search nearest-neighbours using a Btree
- UpdateTree Updates the information in a BTree.
- ProbDistance2PoseDataAboveThreshold p=ProbDistance2PoseDataAboveThreshold(PD,F,t,pt)
- PlotGaussian Plots a Gaussian.
- PlotPoseSequence Plots a sequence of poses.
- ContinueSimulation Continues a previous partial simulation.
- char Converts a pose to a string.
- PlotSensor Plots a sensors.
- get Generic get for relative positioning sensors
- SensorLoopCloses Checks if two sensor readigs can be properly registered.
SOURCE CODE
0001 function out=get(G,field) 0002 % Get function for Gaussians. 0003 % 0004 % Returns the information associated with a Gaussian 0005 % Possible queries are 0006 % - dim: The dimensionality of the space where the Gaussian is defined 0007 % - mean: The mean vector. 0008 % - covariance: The covariance matrix. 0009 % - invCovariance: The inverse of the covariance matrix. 0010 % - volume: The volume of the ellipsoid defined from the covariance matrix. 0011 % - normalizationFactor: The normalization factor for a Gaussian (the constant 0012 % that multiplies the exponential defining the Gaussian 0013 % distribution). 0014 % - ellipse: Returns a line representing the iso-countour of the 0015 % ellipse defined from the mean and covariance at a 90% confidence 0016 % value. For higher-dimensional Gaussians, we marginalize and only 0017 % use the first 2 components of the Gaussians. 0018 0019 switch field 0020 case 'dim' 0021 out=G.dim; 0022 case 'mean' 0023 out=G.m; 0024 case 'covariance' 0025 out=G.S; 0026 case 'invCovariance' 0027 out=G.iS; 0028 case 'volume' 0029 out=G.d; 0030 case 'normalizationFactor' 0031 out=G.ct; 0032 case 'ellipse' 0033 S2d=G.S(1:2,1:2); 0034 % For 2 dim, a Xi squared distribution gives that the 0035 % 90% confidence level is obtained at 2.1459 0036 R=chol(S2d); 0037 y = 2.1459*[cos(0:0.1:2*pi);sin(0:0.1:2*pi)]; 0038 el=R*y; 0039 out = [el el(:,1)]+repmat(G.m(1:2),1,size(el,2)+1); 0040 0041 otherwise 0042 error('Unknow field in Gaussian get'); 0043 end