Public Member Functions | Protected Attributes | List of all members
gazebo::sensors::GaussianNoiseModel Class Reference

Gaussian noise class. More...

#include <GaussianNoiseModel.hh>

Inheritance diagram for gazebo::sensors::GaussianNoiseModel:
Inheritance graph
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Public Member Functions

 GaussianNoiseModel ()
 Constructor. More...
 
virtual ~GaussianNoiseModel ()
 Destructor. More...
 
double ApplyImpl (double _in)
 Apply noise to input data value. More...
 
virtual void Fini ()
 Finalize the noise model. More...
 
double GetBias () const
 Accessor for bias. More...
 
double GetMean () const
 Accessor for mean. More...
 
double GetStdDev () const
 Accessor for stddev. More...
 
virtual void Load (sdf::ElementPtr _sdf)
 Load noise parameters from sdf. More...
 
- Public Member Functions inherited from gazebo::sensors::Noise
 Noise (NoiseType _type)
 Constructor. More...
 
virtual ~Noise ()
 Destructor. More...
 
double Apply (double _in)
 Apply noise to input data value. More...
 
NoiseType GetNoiseType () const
 Accessor for NoiseType. More...
 
virtual void SetCamera (rendering::CameraPtr _camera)
 Set camera needed to create image noise. More...
 
virtual void SetCustomNoiseCallback (boost::function< double(double)> _cb)
 Register a custom noise callback. More...
 

Protected Attributes

double bias
 If type starts with GAUSSIAN, the bias we'll add. More...
 
double mean
 If type starts with GAUSSIAN, the mean of the distribution from which we sample when adding noise. More...
 
double precision
 If type==GAUSSIAN_QUANTIZED, the precision to which the output signal is rounded. More...
 
bool quantized
 True if the type is GAUSSIAN_QUANTIZED. More...
 
double stdDev
 If type starts with GAUSSIAN, the standard deviation of the distribution from which we sample when adding noise. More...
 

Additional Inherited Members

- Public Types inherited from gazebo::sensors::Noise
enum  NoiseType { NONE, CUSTOM, GAUSSIAN }
 Which noise types we support. More...
 

Detailed Description

Gaussian noise class.

Gaussian noise class for image sensors.

Constructor & Destructor Documentation

gazebo::sensors::GaussianNoiseModel::GaussianNoiseModel ( )

Constructor.

virtual gazebo::sensors::GaussianNoiseModel::~GaussianNoiseModel ( )
virtual

Destructor.

Member Function Documentation

double gazebo::sensors::GaussianNoiseModel::ApplyImpl ( double  _in)
virtual

Apply noise to input data value.

This gets overriden by derived classes, and called by Apply.

Parameters
[in]_inInput data value.
Returns
Data with noise applied.

Reimplemented from gazebo::sensors::Noise.

virtual void gazebo::sensors::GaussianNoiseModel::Fini ( )
virtual

Finalize the noise model.

Reimplemented from gazebo::sensors::Noise.

Reimplemented in gazebo::sensors::ImageGaussianNoiseModel.

double gazebo::sensors::GaussianNoiseModel::GetBias ( ) const

Accessor for bias.

Returns
Bias on output.
double gazebo::sensors::GaussianNoiseModel::GetMean ( ) const

Accessor for mean.

Returns
Mean of Gaussian noise.
double gazebo::sensors::GaussianNoiseModel::GetStdDev ( ) const

Accessor for stddev.

Returns
Standard deviation of Gaussian noise.
virtual void gazebo::sensors::GaussianNoiseModel::Load ( sdf::ElementPtr  _sdf)
virtual

Load noise parameters from sdf.

Parameters
[in]_sdfSDF parameters.
[in]_sensorType of sensor.

Reimplemented from gazebo::sensors::Noise.

Reimplemented in gazebo::sensors::ImageGaussianNoiseModel.

Member Data Documentation

double gazebo::sensors::GaussianNoiseModel::bias
protected

If type starts with GAUSSIAN, the bias we'll add.

double gazebo::sensors::GaussianNoiseModel::mean
protected

If type starts with GAUSSIAN, the mean of the distribution from which we sample when adding noise.

double gazebo::sensors::GaussianNoiseModel::precision
protected

If type==GAUSSIAN_QUANTIZED, the precision to which the output signal is rounded.

bool gazebo::sensors::GaussianNoiseModel::quantized
protected

True if the type is GAUSSIAN_QUANTIZED.

double gazebo::sensors::GaussianNoiseModel::stdDev
protected

If type starts with GAUSSIAN, the standard deviation of the distribution from which we sample when adding noise.


The documentation for this class was generated from the following file: