Abstract: A technique for provisioning UAVs to operate in a specific geographical region includes acquiring reference aerial images of the specific geographical region from a secondary source. The reference aerial images have at least one style characteristic that is distinct from that of operational aerial images acquired by the UAVs during operation. The reference aerial images are transformed into training images that adopt a style of the operational aerial images using a neural style transfer. A neural network that facilitates vision-based navigation of the UAVs is trained using the training images. The neural network is trained to operate in the specific geographical region prior to operation in the specific geographical region.

Abstract: A technique for maintaining a terrain model includes: acquiring aerial images of a scene at an area of interest (AOI), wherein the aerial images are acquired by a UAV during a flight mission of the UAV that passes over the AOI; training a ML model onboard the UAV with one or more of the aerial images, wherein the ML model comprises a neural network, which after the training, encodes a volumetric representation of the scene; determining whether the terrain model of the AOI is deemed out-of-date based upon whether the training results in greater than a threshold change in the ML model; and uploading image data acquired by the UAV during the flight mission to a backend data system in response to determining that the terrain model is deemed out-of-date, wherein the image data includes, or is derived from, at least a portion of the aerial images.

Abstract: A technique for using an onboard camera capable of day and night operation on an unmanned aerial vehicle (UAV) includes: traveling along a route of the UAV at night; and acquiring an aerial image with an onboard camera. The onboard camera includes: a sensor device for receiving photons and converting the photons into photoelectrons; a processor for processing the photoelectrons into an image file; a lens positioned adjacent to the sensor device for focusing the photons on the sensor device; and a filter positioned adjacent an outer surface of the lens. The filter can permit photons in the infrared light spectrum to pass to the lens, and attenuate at least a portion of the photons in the visible light spectrum prior to reaching the lens.

Abstract: A method for perception validation of an unmanned aerial vehicle (UAV) includes: acquiring an aerial image of a ground area with an onboard camera system of the UAV, generating a semantic above ground altitude (AGL) estimate with a neural network trained to output the semantic AGL estimate in response to the aerial image fed as an input to the neural network, generating a motion estimate or a position estimate based upon perception sensor data output from a perception sensor disposed onboard the UAV, and cross-validating the motion or position estimate against the semantic AGL estimate.

Abstract: 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.

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.

Abstract: 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.

Abstract: Systems, apparatus, and methods are presented for testing a device. One method includes activating an actuator device to cause a carriage, coupled to a device, to be moved in one or more directions along a guide rail, wherein the device includes at least one processing device and one or more sensor devices. The method may also comprise receiving, by the device, one or more input commands and executing, by the device based on the one or more input commands, a software application to generate an output while the device is moving in the one or more directions. Further, the method may comprise verifying the execution of the software application on the device based on the output.

Abstract: A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method also includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method additionally includes determining, based on the segmentation image, a percentage of obstacle pixels within a surrounding area of a delivery point at the delivery location, wherein each obstacle pixel has a semantic classification indicative of an obstacle in the delivery location. The method further includes based on the percentage of obstacle pixels being above a threshold percentage, aborting a delivery process of the UAV.

Abstract: A technique for using an onboard camera capable of day and night operation on an unmanned aerial vehicle (UAV) includes: traveling along a route of the UAV at night; and acquiring an aerial image with an onboard camera. The onboard camera includes: a sensor device for receiving photons and converting the photons into photoelectrons; a processor for processing the photoelectrons into an image file; a lens positioned adjacent to the sensor device for focusing the photons on the sensor device; and a filter positioned adjacent an outer surface of the lens. The filter can permit photons in the infrared light spectrum to pass to the lens, and attenuate at least a portion of the photons in the visible light spectrum prior to reaching the lens.

Abstract: A method for perception validation of an unmanned aerial vehicle (UAV) includes: acquiring an aerial image of a ground area with an onboard camera system of the UAV, generating a semantic above ground altitude (AGL) estimate with a neural network trained to output the semantic AGL estimate in response to the aerial image fed as an input to the neural network, generating a motion estimate or a position estimate based upon perception sensor data output from a perception sensor disposed onboard the UAV, and cross-validating the motion or position estimate against the semantic AGL estimate.

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.

Abstract: A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method also includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method additionally includes determining, based on the segmentation image, a percentage of obstacle pixels within a surrounding area of a delivery point at the delivery location, wherein each obstacle pixel has a semantic classification indicative of an obstacle in the delivery location. The method further includes based on the percentage of obstacle pixels being above a threshold percentage, aborting a delivery process of the UAV.

Abstract: A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method further includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method also includes determining, based on the segmentation image, a distance-to-obstacle image of a delivery zone at the delivery location. The distance-to-obstacle image comprises a plurality of pixels, each pixel representing a distance in the segmentation image from a nearest pixel area with a semantic classification indicative of an obstacle in the delivery location. Additionally, the method includes selecting, based on the distance-to-obstacle image, a delivery point in the delivery zone. The method also includes positioning the UAV above the delivery point in the delivery zone for delivery of a payload.

Abstract: A method includes navigating, by an unmanned aerial vehicle (UAV), to a first altitude above a first delivery point at a delivery location. The method further includes determining, by the UAV, a second delivery point at the delivery location. The method includes navigating, by the UAV, through a descending trajectory to move the UAV from the first altitude above the first delivery point to a second altitude above the second delivery point at the delivery location. The second altitude is lower than the first altitude. The method additionally includes delivering, by the UAV, a payload to the second delivery point at the delivery location. The method includes after delivering the payload, navigating, by the UAV, through an ascending trajectory to move the UAV from a third altitude above the second delivery point to a fourth altitude above the first delivery point. The fourth altitude is higher than the third altitude.

Abstract: Systems, apparatus, and methods are presented for testing a device. One method includes activating an actuator device to cause a carriage, coupled to a device, to be moved in one or more directions along a guide rail, wherein the device includes at least one processing device and one or more sensor devices. The method may also comprise receiving, by the device, one or more input commands and executing, by the device based on the one or more input commands, a software application to generate an output while the device is moving in the one or more directions. Further, the method may comprise verifying the execution of the software application on the device based on the output.

Abstract: A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method further includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method also includes determining, based on the segmentation image, a distance-to-obstacle image of a delivery zone at the delivery location. The distance-to-obstacle image comprises a plurality of pixels, each pixel representing a distance in the segmentation image from a nearest pixel area with a semantic classification indicative of an obstacle in the delivery location. Additionally, the method includes selecting, based on the distance-to-obstacle image, a delivery point in the delivery zone. The method also includes positioning the UAV above the delivery point in the delivery zone for delivery of a payload.

Abstract: A method includes navigating, by an unmanned aerial vehicle (UAV), to a first altitude above a first delivery point at a delivery location. The method further includes determining, by the UAV, a second delivery point at the delivery location. The method includes navigating, by the UAV, through a descending trajectory to move the UAV from the first altitude above the first delivery point to a second altitude above the second delivery point at the delivery location. The second altitude is lower than the first altitude. The method additionally includes delivering, by the UAV, a payload to the second delivery point at the delivery location. The method includes after delivering the payload, navigating, by the UAV, through an ascending trajectory to move the UAV from a third altitude above the second delivery point to a fourth altitude above the first delivery point. The fourth altitude is higher than the third altitude.

Abstract: A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method also includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method additionally includes determining, based on the segmentation image, a percentage of obstacle pixels within a surrounding area of a delivery point at the delivery location, wherein each obstacle pixel has a semantic classification indicative of an obstacle in the delivery location. The method further includes based on the percentage of obstacle pixels being above a threshold percentage, aborting a delivery process of the UAV.

Abstract: 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.