Skydio, Inc.

Harrison Zheng

Hayk Martirosyan

Jack Louis Zhu

Peter Benjamin Henry

Abraham Galton Bachrach

Adam Parker Bry

Matthew Joseph Donahoe

Brian Richman

Ryan David Kennedy

Himel Mondal

Quentin Allen Wah Yen Delepine

Kristen Marie Holtz

Gareth Benoit Cross

Teodor Tomic

Josiah Timothy VanderMey

Samuel Shenghung Wang

Dominic William Pattison

Bradley Matthew Solliday

Nathan Leo Bucki

Yixiao Sun

Aaron Christopher Miller

A computer of an unmanned aerial vehicle (UAV) accesses, from a memory unit, a problem definition comprising cost functions associated with travel of the UAV. The computer causes movement of the UAV based on the cost functions. The computer adjusts one or more of the cost functions during a flight of the UAV. The computer causes further movement of the UAV based on the adjusted one or more of the cost functions.

Unmanned aerial vehicle operated based on cost functions

Described herein are systems for roof scan using an unmanned aerial vehicle. For example, some methods include capturing, using an unmanned aerial vehicle, an overview image of a roof of a building from above the roof; presenting a suggested bounding polygon overlaid on the overview image to a user; determining a bounding polygon based on the suggested bounding polygon and user edits; based on the bounding polygon, determining a flight path including a sequence of poses of the unmanned aerial vehicle with respective fields of view at a fixed height that collectively cover the bounding polygon; fly the unmanned aerial vehicle to a sequence of scan poses with horizontal positions matching respective poses of the flight path and vertical positions determined to maintain a consistent distance above the roof; and scanning the roof from the sequence of scan poses to generate a three-dimensional map of the roof.

Roof scan using unmanned aerial vehicle

A computer accesses an input element storage and an output element storage. The computer accesses a symbolic expression for output element storage as a function of the input element storage. The computer computes, using a symbolic computation engine of the computer, a symbolic expression for the tangent space Jacobian of the output element storage with respect to an input tangent space. The computer outputs the computed expression.

Computer-implemented symbolic differentiation using first-order retraction

Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; and capturing one or more images of the structure from the pose.

Structure scan using unmanned aerial vehicle

A computer accesses a first symbolic expression for an output matrix as a function of an input matrix at a computing device comprising processing circuitry and memory. The computer computes a first Jacobian of the input matrix with respect to an input tangent space. The computer computes a second Jacobian of the output matrix with respect to the input matrix. The computer computes a third Jacobian of an output tangent space with respect to the input matrix. The computer applies symbolic matrix multiplication to the first Jacobian, the second Jacobian, and the third Jacobian to obtain a second symbolic expression for the output tangent space with respect to the input tangent space. The computer provides a representation of the second symbolic expression, the second symbolic expression representing a computed tangent-space Jacobian.

Computer-implemented computation of tangent-space jacobian

In some examples, one or more processors of an unmanned aerial vehicle (UAV), control a propulsion mechanism of the UAV to cause the UAV to navigate to a plurality of positions in relation to a scan target. Using one or more image sensors of the UAV, a first image of the scan target is captured from a first position of the plurality of positions, and a second image of the scan target is captured from a second position of the plurality of positions. A disparity is determined between the first image captured at the first position and the second image captured at the second position. A three-dimensional model corresponding to the scan target is determined based in part on the disparity determined between the first image and the second image.

Determining a three-dimensional model of a scan target

In some examples, one or more processors of an aerial vehicle access a scan plan including a sequence of poses for the aerial vehicle to assume to capture, using the one or more image sensors, images of a scan target. A next pose of the scan plan is checked for obstructions, and based at least on detection of an obstruction, the one or more processors determine whether a backup pose is available for capturing an image of the targeted point orthogonally along a normal of the targeted point. Responsive to determining that the backup pose is unavailable for capturing an image of the targeted point orthogonally along the normal of the targeted point, image capture of the targeted point is performed at an oblique angle to the normal of the targeted point.

Unmanned aerial vehicle adaptable to obstructions

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.

Indicating a scan target for an unmanned aerial vehicle

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.

Performing 3D reconstruction via an unmanned aerial vehicle

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.

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.

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.

Scanning structures via unmanned aerial vehicles

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.

Growing community of inventors

Palo Alto, CA, United States of America

Harrison Zheng