US 12,405,615 B2
Cloud and hybrid-cloud flight vehicle and robotic control system AI and ML enabled cloud-based software and data system method for the optimization and distribution of flight control and robotic system solutions and capabilities
Thomas Andrew Youmans, Santa Monica, CA (US); Thomas Bradford Doermer Callen, Pasadena, CA (US); and James Croughan, Los Angeles, CA (US)
Assigned to RHOMAN AEROSPACE CORPORATION, Pasadena, CA (US)
Filed by Thomas Andrew Youmans, Santa Monica, CA (US); Thomas Bradford Doermer Callen, Pasadena, CA (US); and James Croughan, Los Angeles, CA (US)
Filed on Jun. 6, 2022, as Appl. No. 17/833,420.
Claims priority of provisional application 63/196,689, filed on Jun. 4, 2021.
Prior Publication US 2022/0390964 A1, Dec. 8, 2022
Int. Cl. G01C 21/20 (2006.01); G01C 21/00 (2006.01); G05D 1/00 (2006.01); B64U 101/30 (2023.01)
CPC G05D 1/104 (2013.01) [G01C 21/005 (2013.01); G01C 21/20 (2013.01); G05D 1/0016 (2013.01); G05D 1/0027 (2013.01); B64U 2101/30 (2023.01); B64U 2201/00 (2023.01); B64U 2201/20 (2023.01)] 19 Claims
OG exemplary drawing
 
1. A system for estimating a location of one or more drones, the system comprising:
a server comprising: a processor, memory accessible by the processor, program instructions and data stored in the server memory;
wherein data stored in the memory comprises a geolocation dataset that comprises:
region data representing a travel region for at least one drone; and
object data representing recorded objects that correspond with real world objects within the travel region, the object data including feature data of recorded objects within the travel region and geo-tagged metadata informing the locations of recorded objects within the travel region;
wherein the server is configured to estimate a location of the at least one drone by:
receiving a drone signal comprising data on one or more inputs received at one or more sensors of the drone;
estimating a geographical location of the drone by:
matching data from the drone signal with object data for one or more recorded objects in the geolocation dataset to identify one or more objects in the drone signal data as matched recorded objects that correspond with recorded objects in the geolocation dataset;
estimating a distance of the drone to the one or more matched recorded objects;
estimating a geographical location of the drone based on the estimated distance of the drone from the one or more matched recorded objects and the geo-tagged metadata of the one or more matched recorded objects; and
estimating an enhanced geographical location of the drone by:
identifying region data corresponding with the estimated geographical location of the drone;
selecting feature data of one or more recorded objects that are associated with the identified region data;
matching data from the drone signal with the selected feature data to identify one or more objects in the drone signal data as second matched recorded objects that correspond with recorded objects in the geolocation dataset;
estimating a distance of the drone to the one or more second matched recorded objects; and
estimating an enhanced geographical location of the drone based on the estimated distance of the drone from the one or more second matched recorded objects and the geo-tagged metadata of the one or more second matched recorded objects.