US 12,315,097 B2
Cross reality system with fast localization
Miguel Andres Granados Velasquez, Thalwil (CH); Javier Victorio Gomez Gonzalez, Zurich (CH); Danying Hu, Sunnyvale, CA (US); Eran Guendelman, Tel Aviv (IL); Ali Shahrokni, San Jose, CA (US); Ashwin Swaminathan, Dublin, CA (US); and Mukta Prasad, Uitikon Waldegg (CH)
Assigned to Magic Leap, Inc., Plantation, FL (US)
Filed by Magic Leap, Inc., Plantation, FL (US)
Filed on Dec. 7, 2022, as Appl. No. 18/077,200.
Application 18/077,200 is a continuation of application No. 17/185,741, filed on Feb. 25, 2021, granted, now 11,551,430.
Claims priority of provisional application 62/981,939, filed on Feb. 26, 2020.
Prior Publication US 2023/0119217 A1, Apr. 20, 2023
Int. Cl. G06T 19/20 (2011.01); G06T 7/38 (2017.01); G06T 15/00 (2011.01); G06T 19/00 (2011.01)
CPC G06T 19/20 (2013.01) [G06T 7/38 (2017.01); G06T 15/005 (2013.01); G06T 19/006 (2013.01); G06T 2219/2004 (2013.01)] 20 Claims
OG exemplary drawing
 
1. 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:
computing a first rough localization for a first collection of features of the plurality of collections of features;
computing, as a first candidate localization of the plurality of candidate localizations, a first refined localization based on the first rough localization;
evaluating one or more criteria;
determining, based on the evaluation of the one or more criteria, whether to perform a second rough localization for a second collection of features of the plurality of collection of features;
when it is determined to not perform the second rough localization for the second collection of features, compute a second refined localization as a second candidate localization of the plurality of candidate localizations based on the first rough localization; and
when it is determined to perform the second rough localization for the second collection of features, compute the second rough localization for the second collection of features and compute the second refined localization as the second candidate localization based on the second rough localization.