RelNavObservablesType

giant.relative_opnav.estimators.estimator_interface_abc:

class giant.relative_opnav.estimators.estimator_interface_abc.RelNavObservablesType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

This enumeration provides options for the basic types of observables generated in Relative OpNav.

This is used to set the values for the RelNavEstimator.observable_type attribute to specify what types of observables are generated by a technique. If you are not implementing your own RelNav technique then you don’t need to worry about this enumeration really.

When using this enumeration you can combine multiple types using a list (so [RelNavObservablesType.RELATIVE_POSITION, RelNavObservablesType.LIMB] would specify a RelNav technique that generates both a relative position measurement for each image-target pair, as well as individual limb observations as bearing measurement).

CENTER_FINDING = 'CENTER-FINDING'

A technique that locates the bearing to the center-of-figure of the target in the image.

LIMB = 'LIMB'

A technique that identifies the bearings to the limbs of a target in an image.

LANDMARK = 'LANDMARK'

A technique that identifies te bearings to the surface features (landmarks) on a target in an image.

RELATIVE_POSITION = 'RELATIVE-POSITION'

A technique that identifies the relative position between the camera and the center-of-figure of the target in an image.

CONSTRAINT = 'CONSTRAINT'

A technique that identifies the bearings to the same feature in multiple images (whether a known feature or an opportunistic one).

Matching features should be labeled with the same key in different images.

CUSTOM = 'CUSTOM'

A technique that doesn’t fall into another category and requires a custom handler.

The custom handler should be stored in the RelNavEstimator.relnav_handler attribute and should at minimum take a RelativeOpNav instance as the first argument.