
def  __init__ (self, robot_params, free_dict, multisensors, use_cov) 

def  calculate_error (self, opt_all_vec) 

def  calculate_full_param_vec (self, opt_param_vec) 

def  calculate_jacobian (self, opt_all_vec) 

def  multisensor_pose_jacobian (self, opt_param_vec, pose_param_vec, multisensor) 

def  single_sensor_params_jacobian (self, opt_param_vec, target_pose_T, target_id, sensor) 

def  split_all (self, opt_all_vec) 

Helpers for computing errors and jacobians
Definition at line 47 of file opt_runner.py.
def calibration_estimation.opt_runner.ErrorCalc.__init__ 
( 

self, 



robot_params, 



free_dict, 



multisensors, 



use_cov 

) 
 
def calibration_estimation.opt_runner.ErrorCalc.calculate_error 
( 

self, 



opt_all_vec 

) 
 
def calibration_estimation.opt_runner.ErrorCalc.calculate_full_param_vec 
( 

self, 



opt_param_vec 

) 
 
Take the set of optimization params, and expand it into the bigger param vec
Definition at line 61 of file opt_runner.py.
def calibration_estimation.opt_runner.ErrorCalc.calculate_jacobian 
( 

self, 



opt_all_vec 

) 
 
Full Jacobian:
The resulting returned jacobian can be thought of as a set of several concatenated jacobians as follows:
[ J_multisensor_0 ]
J = [ J_multisensor_1 ]
[ : ]
[ J_multisensor_M ]
where M is the number of multisensors, which is generally the number of calibration samples collected.
Multisensor Jacobian:
The Jacobian for a given multisensor is created by concatenating jacobians for each sensor
[ J_sensor_m_0 ]
J_multisensor_m = [ J_sensor_m_1 ]
[ : ]
[ J_sensor_m_S ]
where S is the number of sensors in this multisensor.
Sensor Jacobian:
A single sensor jacobian is created by concatenating jacobians for the system parameters and checkerboards
J_sensor_m_s = [ J_params_m_s  J_m_s_pose0  J_m_s_pose1    J_m_s_CB_poseC ]
The number of rows in J_sensor_m_s is equal to the number of rows in this sensor's residual.
J_params_m_s shows how modifying the free system parameters affects the residual.
Each J_m_s_pose_c is 6 columns wide and shows how moving a certain target affects the residual. All
of the J_m_s_pose blocks are zero, except J_sensor_pose_m, since target m is the only target that
was viewed by the sensors in this multisensor.
Definition at line 146 of file opt_runner.py.
def calibration_estimation.opt_runner.ErrorCalc.multisensor_pose_jacobian 
( 

self, 



opt_param_vec, 



pose_param_vec, 



multisensor 

) 
 
Generates the jacobian from a target pose to the multisensor's residual.
Input:
 opt_param_vec: Vector of the free system parameters (the parameters we're actuall optimizing)
 pose_param_vec: Vector of length 6 encoding the target's pose 0:3=translation 3:6=rotation_axis
 multisensor: The actual multisensor definition.
Output:
 J_scaled: An mx6 jacobian, where m is the length of multisensor's residual. There are 6 columns since the
target's pose is encoded using 6 parameters.
If covariance calculations are enabled, then the Jacobian is scaled by sqrt(Gamma), where Gamma
is the information matrix for this measurement.
Definition at line 294 of file opt_runner.py.
def calibration_estimation.opt_runner.ErrorCalc.single_sensor_params_jacobian 
( 

self, 



opt_param_vec, 



target_pose_T, 



target_id, 



sensor 

) 
 
Computes the jacobian from the free system parameters to a single sensor
Inputs:
 opt_param_vec: Vector of the free system parameters (the parameters we're actuall optimizing)
 target_pose_T: The pose of the target that this sensor is viewing
 target_id: String name of the target that this sensor is viewing. This is necessary to generate the
set of feature points on the target
 sensor: The actual sensor definition
Output:
 J_scaled: An mxn jacobian, where m is the length of sensor's residual and n is the length of opt_param_vec.
If covariance calculations are enabled, then the Jacobian is scaled by sqrt(Gamma), where Gamma
is the information matrix for this measurement.
Definition at line 237 of file opt_runner.py.
def calibration_estimation.opt_runner.ErrorCalc.split_all 
( 

self, 



opt_all_vec 

) 
 
Splits the input vector into two parts:
1) opt_param_vec: The system parameters that we're optimizing,
2) full_pose_arr: A Nx6 array where each row is a checkerboard pose
Definition at line 224 of file opt_runner.py.
calibration_estimation.opt_runner.ErrorCalc._expanded_params 

private 
calibration_estimation.opt_runner.ErrorCalc._free_list 

private 
calibration_estimation.opt_runner.ErrorCalc._j_count 

private 
calibration_estimation.opt_runner.ErrorCalc._multisensors 

private 
calibration_estimation.opt_runner.ErrorCalc._robot_params 

private 
calibration_estimation.opt_runner.ErrorCalc._use_cov 

private 
calibration_estimation.opt_runner.ErrorCalc.marker_pub 
The documentation for this class was generated from the following file: