Abstract: Embodiments are described for detecting optical discrepancies associated with image capture analyzing pixels in multiple images corresponding to common points of reference in a physical environment. In an embodiment, photometric error values are averaged over time to compute the mean error at each pixel. Once the estimate of the mean error has a sufficient number of updates above a specified value, the estimate is thresholded to provide a mask of any optical discrepancies occurring in the stereo pair of images. Applications include detecting optical discrepancies in images captured for use by a visual navigation system in guiding an autonomous vehicle (e.g., an unmanned aerial vehicle).

Abstract: In some examples, an unmanned aerial vehicle (UAV) may include one or more processors configured to capture, with one or more image sensors, and while the UAV is in flight, a plurality of images of a target. The one or more processors may compare a first image of the plurality of images with a second image of the plurality of images to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. In addition, the one or more processors may determine, based at least on the difference, and while the UAV is in flight, an update to a three-dimensional model of the target.

Abstract: Methods and systems are disclosed for an unmanned aerial vehicle (UAV) configured to autonomously navigate a physical environment while capturing images of the physical environment. In some embodiments, the motion of the UAV and a subject in the physical environment may be estimated based in part on images of the physical environment captured by the UAV. In response to estimating the motions, image capture by the UAV may be dynamically adjusted to satisfy a specified criterion related to a quality of the image capture.

Abstract: Techniques are described for controlling an autonomous vehicle such as an unmanned aerial vehicle (UAV) using objective-based inputs. In an embodiment, the underlying functionality of an autonomous navigation system is exposed via an application programming interface (API) allowing the UAV to be controlled through specifying a behavioral objective, for example, using a call to the API to set parameters for the behavioral objective. The autonomous navigation system can then incorporate perception inputs such as sensor data from sensors mounted to the UAV and the set parameters using a multi-objective motion planning process to generate a proposed trajectory that most closely satisfies the behavioral objective in view of certain constraints. In some embodiments, developers can utilize the API to build customized applications for the UAV. Such applications, also referred to as “skills,” can be developed, shared, and executed to control behavior of an autonomous UAV and aid in overall system improvement.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may access a scan plan that includes a sequence of poses for the UAV to assume to capture images of a scan target using one or more image sensors. The UAV may check a next pose of the scan plan for obstructions. Responsive to detection of an obstruction, the UAV may determine a backup pose based at least on a field of view of the next pose. The UAV may control a propulsion mechanism to cause the UAV to fly to assume the backup pose. The UAV may capture, based on the backup pose and using the one or more image sensors, one or more images of the scan target.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may identify a scan target. The UAV may navigate to two or more positions in relation to the scan target. The UAV may capture, using one or more image sensors of the UAV, two or more images of the scan target from different respective positions in relation to the scan target. For instance, the two or more respective positions may be selected by controlling a spacing between the two or more respective positions to enable determination of parallax disparity between a first image captured at a first position and a second image captured at a second position of the two or more positions. The UAV may determine a three-dimensional model corresponding to the scan target based in part on the determined parallax disparity of the two or more images including the first image and the second image.

Abstract: In some examples, an image of a scan target is presented in a user interface on a display associated with a computing device. The user interface receives at least one user input indicating at least one point in a perimeter or edge of a volume for encompassing the scan target presented in the image of the scan target. A graphical representation of the volume in relation to the image of the scan target is generated in the user interface. Information for defining a location of at least a portion of the volume in three-dimensional space is sent to an unmanned aerial vehicle (UAV) to cause, at least in part, the UAV to scan at least a portion of the scan target corresponding to the volume.

Abstract: Methods and systems are disclosed for an unmanned aerial vehicle (UAV) configured to autonomously navigate a physical environment while capturing images of the physical environment. In some embodiments, the motion of the UAV and a subject in the physical environment may be estimated based in part on images of the physical environment captured by the UAV. In response to estimating the motions, image capture by the UAV may be dynamically adjusted to satisfy a specified criterion related to a quality of the image capture.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may include one or more processors configured to capture, with one or more image sensors, and while the UAV is in flight, a plurality of images of a target. The one or more processors may compare a first image of the plurality of images with a second image of the plurality of images to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. In addition, the one or more processors may determine, based at least on the difference, and while the UAV is in flight, an update to a three-dimensional model of the target.

Abstract: Embodiments are described for detecting optical discrepancies associated with image capture analyzing pixels in multiple images corresponding to common points of reference in a physical environment. In an embodiment, photometric error values are averaged over time to compute the mean error at each pixel. Once the estimate of the mean error has a sufficient number of updates above a specified value, the estimate is thresholded to provide a mask of any optical discrepancies occurring in the stereo pair of images. Applications include detecting optical discrepancies in images captured for use by a visual navigation system in guiding an autonomous vehicle (e.g., an unmanned aerial vehicle).

Abstract: Techniques are described for controlling an autonomous vehicle such as an unmanned aerial vehicle (UAV) using objective-based inputs. In an embodiment, the underlying functionality of an autonomous navigation system is exposed via an application programming interface (API) allowing the UAV to be controlled through specifying a behavioral objective, for example, using a call to the API to set parameters for the behavioral objective. The autonomous navigation system can then incorporate perception inputs such as sensor data from sensors mounted to the UAV and the set parameters using a multi-objective motion planning process to generate a proposed trajectory that most closely satisfies the behavioral objective in view of certain constraints. In some embodiments, developers can utilize the API to build customized applications for the UAV. Such applications, also referred to as “skills,” can be developed, shared, and executed to control behavior of an autonomous UAV and aid in overall system improvement.

Abstract: Methods and systems are described for new paradigms for user interaction with an unmanned aerial vehicle (referred to as a flying digital assistant or FDA) using a portable multifunction device (PMD) such as smart phone. In some embodiments, a magic wand user interaction paradigm is described for intuitive control of an FDA using a PMD. In other embodiments, methods for scripting a shot are described.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may access a scan plan that includes a sequence of poses for the UAV to assume to capture images of a scan target using one or more image sensors. The UAV may check a next pose of the scan plan for obstructions. Responsive to detection of an obstruction, the UAV may determine a backup pose based at least on a field of view of the next pose. The UAV may control a propulsion mechanism to cause the UAV to fly to assume the backup pose. The UAV may capture, based on the backup pose and using the one or more image sensors, one or more images of the scan target.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may identify a scan target. The UAV may navigate to two or more positions in relation to the scan target. The UAV may capture, using one or more image sensors of the UAV, two or more images of the scan target from different respective positions in relation to the scan target. For instance, the two or more respective positions may be selected by controlling a spacing between the two or more respective positions to enable determination of parallax disparity between a first image captured at a first position and a second image captured at a second position of the two or more positions. The UAV may determine a three-dimensional model corresponding to the scan target based in part on the determined parallax disparity of the two or more images including the first image and the second image.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may determine, based on a three-dimensional (3D) model including a plurality of points corresponding to a scan target, a scan plan for scanning at least a portion of the scan target. For instance, the scan plan may include a plurality of poses for the UAV to assume to capture images of the scan target. The UAV may capture with one or more image sensors, one or more images of the scan target from one or more poses of the plurality of poses. Further, the UAV may determine an update to the 3D model based at least in part on the one or more images. Additionally, the UAV may update the scan plan based at least in part on the update to the 3D model.

Abstract: Methods and systems are disclosed for an unmanned aerial vehicle (UAV) configured to autonomously navigate a physical environment while capturing images of the physical environment. In some embodiments, the motion of the UAV and a subject in the physical environment may be estimated based in part on images of the physical environment captured by the UAV. In response to estimating the motions, image capture by the UAV may be dynamically adjusted to satisfy a specified criterion related to a quality of the image capture.

Abstract: Methods and systems are described for new paradigms for user interaction with an unmanned aerial vehicle (referred to as a flying digital assistant or FDA) using a portable multifunction device (PMD) such as smart phone. In some embodiments, a magic wand user interaction paradigm is described for intuitive control of an FDA using a PMD. In other embodiments, methods for scripting a shot are described.

Abstract: In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

Abstract: Embodiments are described for detecting optical discrepancies associated with image capture analyzing pixels in multiple images corresponding to common points of reference in a physical environment. In an embodiment, photometric error values are averaged over time to compute the mean error at each pixel. Once the estimate of the mean error has a sufficient number of updates above a specified value, the estimate is thresholded to provide a mask of any optical discrepancies occurring in the stereo pair of images. Applications include detecting optical discrepancies in images captured for use by a visual navigation system in guiding an autonomous vehicle (e.g., an unmanned aerial vehicle).

Abstract: Methods and systems are disclosed for an unmanned aerial vehicle (UAV) configured to autonomously navigate a physical environment while capturing images of the physical environment. In some embodiments, the motion of the UAV and a subject in the physical environment may be estimated based in part on images of the physical environment captured by the UAV. In response to estimating the motions, image capture by the UAV may be dynamically adjusted to satisfy a specified criterion related to a quality of the image capture.