Public Member Functions
def	__init__
def	build_sparsity_dict
def	compute_cov
def	compute_expected
def	compute_expected_J
def	compute_marginal_gamma_sqrt
def	compute_residual
def	compute_residual_scaled
def	get_chain_cov
def	get_measurement
def	get_residual_length
def	update_config
Public Attributes
	sensor_id
	sensor_type
	terms_per_sample
Private Member Functions
def	_compute_expected
Private Attributes
	_camera
	_config_dict
	_full_chain
	_M_cam
	_M_chain

Detailed Description

Definition at line 89 of file camera_chain_sensor.py.

Constructor & Destructor Documentation

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.__init__	(	self,
		config_dict,
		M_cam,
		M_chain
	)

Generates a single sensor block for a single configuration
Inputs:
- config_dict: The configuration for this specific sensor. This looks exactly like
       a single element from the valid_configs list passed into CameraChainBundler.__init__
- M_cam: The camera measurement of type calibration_msgs/CameraMeasurement
- M_chain: The chain measurement of type calibration_msgs/ChainMeasurement

Definition at line 90 of file camera_chain_sensor.py.

Member Function Documentation

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor._compute_expected	(	self,
		chain_state,
		target_pts
	)	`[private]`

Compute what measurement we would expect to see, based on the current system parameters
and the specified target point locations.

Inputs:
- chain_state: The joint angles of this sensor's chain of type sensor_msgs/JointState.
- target_pts: 4XN matrix, storing features point locations in world cartesian homogenous coordinates.

Returns: target points projected into pixel coordinates, in a Nx2 matrix

Definition at line 188 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.build_sparsity_dict ( self )

Build a dictionary that defines which parameters will in fact affect this measurement.

Returned dictionary should be of the following form:
  transforms:
    my_transformA: [1, 1, 1, 1, 1, 1]
    my_transformB: [1, 1, 1, 1, 1, 1]
  chains:
    my_chain:
      gearing: [1, 1, ---, 1]
  rectified_cams:
    my_cam:
      baseline_shift: 1
      f_shift: 1
      cx_shift: 1
      cy_shift: 1

Definition at line 272 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.compute_cov	(	self,
		target_pts
	)

Computes the measurement covariance in pixel coordinates for the given
set of target points (target_pts)
Input:
 - target_pts: 4xN matrix, storing N feature points of the target, in homogeneous coords

Definition at line 229 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.compute_expected	(	self,
		target_pts
	)

Compute the expected pixel coordinates for a set of target points.
target_pts: 4xN matrix, storing feature points of the target, in homogeneous coords
Returns: target points projected into pixel coordinates, in a Nx2 matrix

Definition at line 178 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.compute_expected_J	(	self,
		target_pts
	)

The output Jacobian J shows how moving target_pts in cartesian space affects
the expected measurement in (u,v) camera coordinates.
For n points in target_pts, J is a 2nx3n matrix
Note: This doesn't seem to be used anywhere, except maybe in some drawing code

Definition at line 206 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.compute_marginal_gamma_sqrt	(	self,
		target_pts
	)

Calculates the square root of the information matrix for the measurement of the
current set of system parameters at the passed in set of target points.

Definition at line 146 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.compute_residual	(	self,
		target_pts
	)

Computes the measurement residual for the current set of system parameters and target points
Input:
- target_pts: 4XN matrix, storing features point locations in world cartesian homogenous coordinates.
Output:
- r: terms_per_sample*N long vector, storing pixel residuals for the target points in the form of
     [u1, v1, u2, v2, ..., uN, vN] or [u1, v1, u'1, u2....]

Definition at line 122 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.compute_residual_scaled	(	self,
		target_pts
	)

Computes the residual, and then scales it by sqrt(Gamma), where Gamma
is the information matrix for this measurement (Cov^-1).

Definition at line 136 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.get_chain_cov	(	self,
		target_pts,
		epsilon = `1e-8`
	)

Definition at line 253 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.get_measurement ( self )

Get the target's pixel coordinates as measured by the actual sensor

Definition at line 169 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.get_residual_length ( self )

Definition at line 166 of file camera_chain_sensor.py.

def calibration_estimation.sensors.camera_chain_sensor.CameraChainSensor.update_config	(	self,
		robot_params
	)

On each optimization cycle the set of system parameters that we're optimizing over will change. Thus,
after each change in parameters we must call update_config to ensure that our calculated residual reflects
the newest set of system parameters.

Definition at line 112 of file camera_chain_sensor.py.