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| def | __init__ (self, robot_params, free_dict, multisensors, use_cov) |
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| def | calculate_error (self, opt_all_vec) |
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| def | calculate_full_param_vec (self, opt_param_vec) |
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| def | calculate_jacobian (self, opt_all_vec) |
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| def | multisensor_pose_jacobian (self, opt_param_vec, pose_param_vec, multisensor) |
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| def | single_sensor_params_jacobian (self, opt_param_vec, target_pose_T, target_id, sensor) |
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| def | split_all (self, opt_all_vec) |
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Helpers for computing errors and jacobians
Definition at line 47 of file opt_runner.py.
| def calibration_estimation.opt_runner.ErrorCalc.__init__ |
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self, |
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robot_params, |
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free_dict, |
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multisensors, |
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use_cov |
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) |
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| def calibration_estimation.opt_runner.ErrorCalc.calculate_error |
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self, |
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opt_all_vec |
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) |
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| def calibration_estimation.opt_runner.ErrorCalc.calculate_full_param_vec |
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self, |
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opt_param_vec |
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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 |
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self, |
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opt_all_vec |
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) |
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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 |
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self, |
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opt_param_vec, |
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pose_param_vec, |
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multisensor |
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) |
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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 |
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self, |
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opt_param_vec, |
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target_pose_T, |
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target_id, |
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sensor |
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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 |
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self, |
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opt_all_vec |
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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 |
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private |
| calibration_estimation.opt_runner.ErrorCalc._free_list |
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private |
| calibration_estimation.opt_runner.ErrorCalc._j_count |
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private |
| calibration_estimation.opt_runner.ErrorCalc._multisensors |
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private |
| calibration_estimation.opt_runner.ErrorCalc._robot_params |
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private |
| calibration_estimation.opt_runner.ErrorCalc._use_cov |
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private |
| calibration_estimation.opt_runner.ErrorCalc.marker_pub |
The documentation for this class was generated from the following file: