Cross reality system with fast localization

A method of computing a pose between a first component and a map, wherein a pose of the first component is represented by a plurality of collections of features posed with respect to a coordinate frame of the first component, the method comprising:

• computing a plurality of candidate localizations based on respective collections of features of the plurality of collections of features;
• and
• determining a localization based on the plurality of candidate localizations,
• wherein, computing the plurality of candidate localizations comprises:
• for a first portion of the plurality of collections of features:
• computing a first rough localization for a collection of features in the first portion of the plurality of collections of features;
• and
• computing, as a first candidate localization of the plurality of candidate localizations, a first refined localization based on the first rough localization;
• and
• for a second portion of the plurality of collections of features:
• evaluating one or more criteria to determine whether to compute, as a second candidate localization of the plurality of candidate localizations, a second refined localization based on the first rough localization.

A non-transitory computer-readable medium comprising computer-executable instructions configured to, when executed by at least one processor, perform a method of computing a pose between a first component and a map, wherein a pose of the first component is represented by a plurality of collections of features posed with respect to a coordinate frame of the first component, the method comprising:

• computing a plurality of candidate localizations based on respective collections of features of the plurality of collections of features;
• and
• determining a localization based on the plurality of candidate localizations,
• wherein, computing the plurality of candidate localizations comprises:
• for a first portion of the plurality of collections of features:
• computing a first rough localization for a collection of features in the first portion of the plurality of collections of features;
• and
• computing, as a first candidate localization of the plurality of candidate localizations, a first refined localization based on the first rough localization;
• and
• for a second portion of the plurality of collections of features:
• evaluating one or more criteria to determine whether to compute, as a second candidate localization of the plurality of candidate localizations, a second refined localization based on the first rough localization.