US 2025/0232467 A1Published application

Computer vision classifier defined path planning for unmanned aerial vehicles

AssigneeTomahawk Robotics, Inc.

InventorsWilliam S. BOWMAN, Mark B. MOFFETT, Andrew D. FALENDYSZ, Michael E. BOWMAN, Michael W. HOLT, Timothy M. WILLIAMS, Matthew R. DANKO, Matthew D. SUMMER

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Abstract

Methods and systems are described herein for enabling aerial vehicle navigation in GPS-denied areas. The system may use a camera to record images of terrain as the aerial vehicle is flying to a target location. The system may then detect (e.g., using a machine learning model) objects within those images and compare those objects with objects within an electronic map that was loaded onto the aerial vehicle. When the system finds one or more objects within the electronic map that match the objects detected within the recorded images, the system may retrieve locations (e.g., GPS coordinates) of the objects within the electronic map and calculate, based on the coordinates, the location of the aerial vehicle. Once the location of the aerial vehicle is determined, the system may navigate to a target location or otherwise adjust a flight path of the aerial vehicle.

Claims20

Dependent claims

A system for adjusting flight paths using terrain guidance, the system comprising:

one or more processors;
and
one or more non-transitory, computer-readable storage media storing instructions, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, at an aerial vehicle, an image recorded by an onboard camera of the aerial vehicle, wherein the onboard camera is configured to record images of terrain below the aerial vehicle;
inputting the image into a machine learning model to detect a first plurality of objects with the image, wherein the machine learning model is trained to detect objects within received images;
retrieving an electronic map comprising a second plurality of objects, wherein each object within the second plurality of objects is associated with a location, and wherein each object of the second plurality of objects is represented by a corresponding stored shape;
determining, based on each corresponding shape, whether one or more objects within the image match the one or more objects within the electronic map;
in response to determining that the one or more objects within the image match the one or more objects within the electronic map, determining, based on object locations for the one or more objects, a vehicle location for the aerial vehicle;
and
generating a flight instruction based on the vehicle location and a target location.

7 more elements

A method comprising:

receiving, at an aerial vehicle, an image recorded by an onboard camera of the aerial vehicle, wherein the onboard camera is configured to record images of terrain below the aerial vehicle;
inputting the image into a machine learning model to detect a first plurality of objects with the image, wherein the machine learning model is trained to detect objects within received images;
retrieving an electronic map comprising a second plurality of objects, wherein each object within the second plurality of objects is associated with a location, and wherein each object of the second plurality of objects is represented by a corresponding stored shape;
determining, based on each corresponding shape, whether one or more objects within the image match the one or more objects within the electronic map;
in response to determining that the one or more objects within the image match the one or more objects within the electronic map, determining, based on object locations for the one or more objects, a vehicle location for the aerial vehicle;
and
generating a flight instruction based on the vehicle location and a target location.

4 more elements

One or more non-transitory, computer-readable media storing instructions thereon, wherein the instructions cause one or more processors to perform operations comprising:

receiving, at an aerial vehicle, an image recorded by an onboard camera of the aerial vehicle, wherein the onboard camera is configured to record images of terrain below the aerial vehicle;
inputting the image into a machine learning model to detect a first plurality of objects with the image, wherein the machine learning model is trained to detect objects within received images;
retrieving an electronic map comprising a second plurality of objects, wherein each object within the second plurality of objects is associated with a location, and wherein each object of the second plurality of objects is represented by a corresponding stored shape;
determining, based on each corresponding shape, whether one or more objects within the image match the one or more objects within the electronic map;
in response to determining that the one or more objects within the image match the one or more objects within the electronic map, determining, based on object locations for the one or more objects, a vehicle location for the aerial vehicle;
and
generating a flight instruction based on the vehicle location and a target location.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/172,411Filed 7 Apr 2025
Priority: 8 Aug 2023
Published: 17 Jul 2025
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06T 7/70
Image analysis: determining position or orientation of objects or cameras
G06T 7/75
Using feature-based methods: involving models
G06T 7/50
Image analysis: depth or shape recovery
G06T 7/90
Image analysis: determination of colour characteristics

Family

Application18/446,450Continuation · filed 8 Aug 2023
Application19/172,411Continuation
US 12,293,538 B2Related patent
US 2025/0054175 A1Published application
US 2025/0232467 A1This patent

From the US filing history. Foreign family members aren’t listed.