公开(公告)号：US20250130581A1

公开(公告)日：2025-04-24

申请号：US18382806

申请日：2023-10-23

Applicant: WING Aviation LLC

Inventor： Dinuka Abeywardena ,  Konstantin Bozhkov ,  Linhao Jin ,  Domitille Commun ,  Xinzhi Fan ,  Kyle Krafka

IPC: G06V10/82 ,  B64U10/20 ,  B64U101/30 ,  B64U101/60 ,  G06F30/15 ,  G06V10/774 ,  G06V20/17

Abstract: A method of operation of an unmanned aerial vehicle (UAV) service includes acquiring aerial images of a scene at an area of interest (AOI), wherein the aerial images are acquired with a UAV of the UAV service during a flight mission of the UAV that passes over the AOI; uploading a mission log of the flight mission to a backend data system of the UAV service, the mission log including image data that includes, or is derived from, at least a portion of the aerial images; and training a neural radiance field (NeRF) model with one or more of the aerial images, wherein the NeRF model comprises a neural network, which after the training, encodes a volumetric representation of the scene capable of generating novel views of the scene different than any of the aerial images used to train the NeRF model.

公开(公告)号：US20230142863A1

公开(公告)日：2023-05-11

申请号：US17521625

申请日：2021-11-08

Applicant: WING AVIATION LLC

Inventor： Xinzhi Fan ,  Zaven Muradyan

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/00

CPC classification number: G06N3/088 ,  G06N3/0454 ,  G06N3/006

Abstract: In some embodiments, a computer-implemented method for simulating an unmanned aerial vehicle (UAV) to improve control system performance is provided. A computing system obtains ground truth aerial imagery for a region that depicts the region during a first state. The computing system determines a route for a simulated UAV within the region. The computing system generates, based on the ground truth aerial imagery, predicted aerial imagery that depicts portions of the region associated with the route. The computing system generates simulated aerial imagery that depicts portions of the region associated with the route during a second state different from the first state by providing the predicted aerial imagery to a machine learning model. The computing system simulates travel of the simulated UAV along the route during the second state by providing the simulated aerial imagery as simulated input to at least one control system of the simulated UAV.