struct mrpt::maps::COccupancyGridMap2D::TLaserSimulUncertaintyParams
Input params for laserScanSimulatorWithUncertainty()
#include <mrpt/maps/COccupancyGridMap2D.h> struct TLaserSimulUncertaintyParams { // fields double UT_alpha {0.99}; double UT_kappa {.0}; double UT_beta {2.0}; size_t MC_samples {10}; mrpt::poses::CPosePDFGaussian robotPose; float aperture {M_PIf}; bool rightToLeft {true}; float maxRange {80.f}; mrpt::poses::CPose3D sensorPose; size_t nRays {361}; float rangeNoiseStd {.0f}; float angleNoiseStd {.0f}; unsigned int decimation {1}; float threshold {.6f}; TLaserSimulUncertaintyMethod method {sumUnscented}; // construction TLaserSimulUncertaintyParams(); };
Fields
double UT_alpha {0.99}
[sumUnscented] UT parameters.
Defaults: alpha=0.99, kappa=0, betta=2.0
size_t MC_samples {10}
[sumMonteCarlo] MonteCarlo parameter: number of samples (Default: 10)
mrpt::poses::CPosePDFGaussian robotPose
The robot pose Gaussian, in map coordinates.
Recall that sensor pose relative to this robot pose must be specified in the observation object
float aperture {M_PIf}
(Default: M_PI) The “aperture” or field-of-view of the range finder, in radians (typically M_PI = 180 degrees).
bool rightToLeft {true}
(Default: true) The scanning direction: true=counterclockwise; false=clockwise
float maxRange {80.f}
(Default: 80) The maximum range allowed by the device, in meters (e.g.
80m, 50m,…)
mrpt::poses::CPose3D sensorPose
(Default: at origin) The 6D pose of the sensor on the robot at the moment of starting the scan.
float rangeNoiseStd {.0f}
(Default: 0) The standard deviation of measurement noise.
If not desired, set to 0
float angleNoiseStd {.0f}
(Default: 0) The sigma of an optional Gaussian noise added to the angles at which ranges are measured (in radians)
unsigned int decimation {1}
(Default: 1) The rays that will be simulated are at indexes: 0, D, 2D, 3D,…
float threshold {.6f}
(Default: 0.6f) The minimum occupancy threshold to consider a cell to be occupied
TLaserSimulUncertaintyMethod method {sumUnscented}
(Default: sumMonteCarlo) Select the method to do the uncertainty propagation