Abstract: Disclosed are methods and systems for utilizing an uncertainty map to select a best set of optical flow estimates for one or more vehicles. For instance, a method may include receiving one or more images captured by one or more cameras of at least one vehicle, analyzing the one or more images by inputting the one or more images into a trained machine-learning model, wherein the trained machine-learning model is configured to process the one or more images to determine an optical flow map and an uncertainty map, determining a feature map based on the optical flow map, determining an improved feature map by applying the uncertainty map to the feature map, transforming the improved feature map by applying at least one transformation or optimization to the improved feature map, and based on the transformed improved feature map, estimating a pose value, a velocity value, or an attitude value.

Abstract: Systems and methods for low-cost HAGL measurement and terrain database updates are provided. In one example, a system includes an image capturing device configured to capture first and second images and a navigation system configured to generate pose data. The system further includes processor(s) communicatively coupled to the image capturing device and the navigation system and a non-transitory, computer-readable medium communicatively coupled to the processor(s) that stores instruction(s) which, when executed by the processor(s), cause the processor(s) to: estimate optical flow between the first and second images; determine rigid flow per image for each depth bin of a group of depth bins based on the pose data, wherein each depth bin corresponds to a particular depth range; and generate a dense depth map by determining a depth bin, for each pixel, that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel.

Abstract: Disclosed are methods and systems for utilizing an uncertainty map to select a best set of optical flow estimates for one or more vehicles. For instance, a method may include receiving one or more images captured by one or more cameras of at least one vehicle, analyzing the one or more images by inputting the one or more images into a trained machine-learning model, wherein the trained machine-learning model is configured to process the one or more images to determine an optical flow map and an uncertainty map, determining a feature map based on the optical flow map, determining an improved feature map by applying the uncertainty map to the feature map, transforming the improved feature map by applying at least one transformation or optimization to the improved feature map, and based on the transformed improved feature map, estimating a pose value, a velocity value, or an attitude value.

Abstract: A smart landing pad comprises a flexible display that shows images or patterns, and a protective layer over the display. The protective layer allows a UAV to land without damaging the display. Locator and range finder devices, coupled to the display, communicate with the UAV. The display is operative for wireless communications with a computer or mobile device that provides on-demand user functions, allowing for dynamically changing or customizing the images/patterns shown on the display. The images/patterns comprise a background area showing changeable images that match an environment where the landing pad is placed, and a target landing area surrounded by the background area. The target landing area includes a changeable insensitive, contrast portion, and changeable marker pattern portions having changeable colors/shapes. The images/patterns also include changeable QR codes on the target landing area. The display is IoT enabled so that data from the landing pad is remotely cloud accessible.

Abstract: In some examples, a system includes a memory configured to store a first image and a second image captured by one or more cameras mounted on one or more vehicles and store locations and orientations of the one or more cameras at times when the first and second images were captured. The system also includes processing circuitry configured to identify an existing landmark in the first and second images. The processing circuitry is also configured to determine a latitude, a longitude, and an altitude of the existing landmark based on the locations and orientations of the one or more cameras at the times when the images were captured. The processing circuitry is configured to create a file including the location of the existing landmark and pixel coordinates of the existing landmark in the first and second images.

Abstract: A computing system obtains asset information, unmanned aerial vehicle (UAV) information, and regulation information, and determines, based on the obtained information, a 3-D global asset-inspection plan including a plurality of UAV route segments and respective UAV launch locations.

Abstract: In some examples, a system includes at least three receivers configured to receive a surveillance packet from another vehicle at respective arrival times. The system includes a first receiver configured to receive a surveillance packet from the other vehicle at a first time. The system also includes a second receiver configured to receive the surveillance packet from the other vehicle at a second time. The system further include a third receiver configured to receive the surveillance packet from the other vehicle at a third time. In addition, the system includes processing circuitry configured to determine a location of the ownship vehicle based on the received surveillance packet and the respective arrival times.

Abstract: In some examples, a system includes at least three receivers configured to receive a surveillance packet from another vehicle at respective arrival times. The system includes a first receiver configured to receive a surveillance packet from the other vehicle at a first time. The system also includes a second receiver configured to receive the surveillance packet from the other vehicle at a second time. The system further include a third receiver configured to receive the surveillance packet from the other vehicle at a third time. In addition, the system includes processing circuitry configured to determine a location of the ownship vehicle based on the received surveillance packet and the respective arrival times.

Abstract: A smart landing pad comprises a flexible display that shows images or patterns, and a protective layer over the display. The protective layer allows a UAV to land without damaging the display. Locator and range finder devices, coupled to the display, communicate with the UAV. The display is operative for wireless communications with a computer or mobile device that provides on-demand user functions, allowing for dynamically changing or customizing the images/patterns shown on the display. The images/patterns comprise a background area showing changeable images that match an environment where the landing pad is placed, and a target landing area surrounded by the background area. The target landing area includes a changeable insensitive, contrast portion, and changeable marker pattern portions having changeable colors/shapes. The images/patterns also include changeable QR codes on the target landing area. The display is IoT enabled so that data from the landing pad is remotely cloud accessible.

Abstract: An augmented-reality head-mounted display is configured to display an indication of a location of an unmanned aerial vehicle with respect to the field-of-view of the head-mounted display, in order to assist a UAV operator to maintain line-of-sight with the UAV.

Abstract: An augmented-reality head-mounted display is configured to display an indication of a location of an unmanned aerial vehicle with respect to the field-of-view of the head-mounted display, in order to assist a UAV operator to maintain line-of-sight with the UAV.

Abstract: In some examples, a system includes a memory configured to store a first image and a second image captured by one or more cameras mounted on one or more vehicles and store locations and orientations of the one or more cameras at times when the first and second images were captured. The system also includes processing circuitry configured to identify an existing landmark in the first and second images. The processing circuitry is also configured to determine a latitude, a longitude, and an altitude of the existing landmark based on the locations and orientations of the one or more cameras at the times when the images were captured. The processing circuitry is configured to create a file including the location of the existing landmark and pixel coordinates of the existing landmark in the first and second images.

Abstract: A computing system obtains asset information, unmanned aerial vehicle (UAV) information, and regulation information, and determines, based on the obtained information, a 3-D global asset-inspection plan including a plurality of UAV route segments and respective UAV launch locations.