Wing Aviation LLC

Dinuka Abeywardena

Damien Bruno Jourdan

Kyle Krafka

Ali Shoeb

Martin Kubie

Konstantin Bozhkov

Jesse Blake

James Ryan Burgess

James Schmalzried

Stephen Lacy

Brandon L Jones

Kyle Julian

Aditya Undurti

Sai Bhargav Yalamanchi

Marco Caflisch

Kyle Kakligian

Xinzhi Fan

Scott Barron

Linhao Jin

Brandon Jones

A technique of camera exposure control for vision-based navigation of an unmanned aerial vehicle (UAV) includes acquiring an aerial image of a ground area below the UAV with an onboard camera system of the UAV, estimating a visual motion factor based on a speed of the UAV and an altitude of the UAV, and adjusting an exposure control setting of the onboard camera system based on the visual motion factor.

Adaptive camera exposure control for navigating a UAV in low light conditions

An unmanned aerial vehicle (UAV) includes a propulsion system, a global navigation satellite system (GNSS) sensor, a camera and a controller. The controller includes logic that, in response to execution by the controller, causes the UAV to in response to detecting a loss of tracking by the GNSS sensor determine an estimated location of the UAV on a map based on a location image captured by the camera, determine a route to a destination using tracking parameters embedded in the map, wherein the map is divided into a plurality of sections and the tracking parameters indicate an ease of determining a location of the UAV using images captured by the camera with respect to each section, and control the propulsion system to cause the UAV to follow the route to the destination.

Map including data for routing aerial vehicles during GNSS failure

A method includes receiving a digital surface model of an area for unmanned aerial vehicle (UAV) navigation. The digital surface model represents an environmental surface in the area. The method includes determining, for each grid cell of a plurality of grid cells in the area, a confidence value of an altitude of the environmental surface at the grid cell and determining a terrain clearance value based at least on the confidence value of the altitude of the environmental surface at the grid cell. The method includes determining a route for a UAV through the area such that the altitude of the UAV is above the altitude of the environmental surface at each grid cell of a sequence of grid cells of the route by at least the terrain clearance value determined for the grid cell. The method includes causing the UAV to navigate through the area using the determined route.

Processes for generating and updating flyable airspace for unmanned aerial vehicles

Described is a method that involves operating an unmanned aerial vehicle (UAV) to begin a flight, where the UAV relies on a navigation system to navigate to a destination. During the flight, the method involves operating a camera to capture images of the UAV's environment, and analyzing the images to detect features in the environment. The method also involves establishing a correlation between features detected in different images, and using location information from the navigation system to localize a feature detected in different images. Further, the method involves generating a flight log that includes the localized feature. Also, the method involves detecting a failure involving the navigation system, and responsively operating the camera to capture a post-failure image. The method also involves identifying one or more features in the post-failure image, and determining a location of the UAV based on a relationship between an identified feature and a localized feature.

Backup navigation system for unmanned aerial vehicles

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.

Using NeRF models to facilitate operations of a UAV delivery service

A technique for detecting an environmental change to a delivery zone via an unmanned aerial vehicle includes obtaining an anchor image and an evaluation image, each representative of the delivery zone, providing the anchor image and the evaluation image to a machine learning model to determine an embedding score associated with a distance between representations of the anchor image and the evaluation image within an embedding space, and determining an occurrence of the environmental change to the delivery zone when the embedding score is greater than a threshold value.

Detection of environmental changes to delivery zone

A technique for validating a balcony to receive delivery of a parcel via a UAV includes obtaining a first identification of a general location of the balcony; generating a first image representing a building including the balcony where the first image is selected based upon the location identified; obtaining a second identification or a confirmation of a precise location of the balcony in the building where the second identification or the confirmation are received in response to an end-user interaction with the first image; determining a deliverability score based at least in part on the precise location of the balcony; and indicating an enrollment status to the end-user where the enrollment status is generated based upon the deliverability score.

UAV balcony deliveries to multi-level buildings

In some embodiments, aerial images of a geographic area are captured by an autonomous vehicle. In some embodiments, the locations of structures within a subset of the aerial images are manually annotated, and geographical locations of the manual annotations are determined based on pose information of the camera. In some embodiments, a machine learning model is trained using the manually annotated aerial images. The machine learning model is used to automatically generate annotations of other images of the geographic area, and the geographical locations determined from the manual annotations are used to determine an accuracy probability of the automatic annotations. The automatic annotations determined to be accurate may be used to re-train the machine learning model to increase its precision and recall.

Systems and methods for generating annotations of structured, static objects in aerial imagery using geometric transfer learning and probabilistic localization

Systems and methods for image based localization for unmanned aerial vehicles (UAVs) are disclosed. In one embodiment, a method for navigating a UAV includes: flying a UAV along a flight path; acquiring an image of a ground area along the flight path with a camera carried by the UAV; and sending the image to a base station. The method further includes receiving navigation data from the base station, based upon a comparison of the image of the ground area to at least one terrestrial map of the flight path.

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Mountain View, CA, United States of America

Dinuka Abeywardena