Abstract: Described herein are unmanned aerial vehicles (UAVs), systems, and methods for capturing panoramic images using cameras onboard a UAV. For example, an embodiment pertains to a UAV including a flight control system, a propulsion system operatively coupled with the flight control system, and an image system comprising navigational cameras and a gimbal camera. The image system is configured to capture a first set of images of a scene using the navigational cameras, stitch the first set of images together to create a first panoramic image of the scene, identify a flight plan for capturing a second set of images with which to create a second panoramic image of the scene using the gimbal camera, capture the second set of images of the scene using the gimbal camera, and stitch the second set of images together to create the second panoramic image.

Abstract: Technology for generating and displaying a graphical user interface for operating an unmanned aerial vehicle (UAV) is disclosed herein that generates and updates a representation of a spline flight path. In various implementations, a graphical user interface detects user interactions with a remote control device directing the flight control subsystem of the UAV to record keyframes and to compute a spline based on the keyframes during flight. The graphical user interface displays a real-time perspective of the UAV with a representation of the spline and the keyframes overlaying the view. The graphical user interface continually updates the representation as the UAV flies and when the spline is updated as the keyframes are updated.

Abstract: A technique is introduced for touchdown detection during autonomous landing by an aerial vehicle. In some embodiments, the introduced technique includes processing perception inputs with a dynamics model of the aerial vehicle to estimate the external forces and/or torques acting on the aerial vehicle. The estimated external forces and/or torques are continually monitored while the aerial vehicle is landing to determine when the aerial vehicle is sufficiently supported by a landing surface. In some embodiments, semantic information associated with objects in the environment is utilized to configure parameters associated with the touchdown detection process.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may determine a first acceleration of the UAV based at least on information from an onboard accelerometer received at least one of prior to or during takeoff. The UAV may determine a second acceleration of the UAV based at least on location information received via a satellite positioning system receiver at least one of prior to or during takeoff. The UAV may further determine a relative heading of the UAV based at least in part on the first acceleration and the second acceleration, and may be directed to navigate an environment based at least on the determined relative heading.

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: Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence, training, and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

Abstract: A technique is described for developing and using applications and skills with an autonomous vehicle. In an example embodiment, a development platform is provided that enables access to a developer console for developing software modules for use with an autonomous vehicle. Using the developer console, a developer user can specify instructions for causing an autonomous vehicle to perform one or more operations. For example, to control the behavior of an autonomous vehicle, the instructions can cause an executing computer system at the autonomous vehicle to generate calls to an application programming interface (API) associated with an autonomous navigation system of autonomous vehicle. Such calls to the API can be configured to adjust a parameter of a behavioral objective associated with a trajectory generation process performed by the autonomous navigation system that controls the behavior of the autonomous vehicle.

Abstract: A calibration of an unmanned aerial vehicle is performed without the use of a magnetometer. The unmanned aerial vehicle generates a first acceleration vector in a navigation frame of reference and a second acceleration vector in a GPS frame of reference. The unmanned aerial vehicle estimates a heading of the unmanned aerial vehicle based on the first acceleration vector and the second acceleration vector. The unmanned aerial vehicle performs a calibration based on the estimated heading of the 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: 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.

Abstract: An unmanned aerial vehicle (UAV) comprises a flight control system and an electromechanical system directed by the flight control system. The flight control system is configured to track a position of a beacon that is in motion and monitor a difference between an actual position of the unmanned aerial vehicle and a desired position of the unmanned aerial vehicle relative to the position of the beacon. The flight control system configures one or more flight objectives based on one or more factors comprising whether the difference between the actual position and the desired position exceeds a threshold, wherein the flight objectives comprise a velocity objective and a position objective. The flight control system also commands the electromechanical system based at least on the one or more flight objectives.

Abstract: A technique is introduced for touchdown detection during autonomous landing by an aerial vehicle. In some embodiments, the introduced technique includes processing perception inputs with a dynamics model of the aerial vehicle to estimate the external forces and/or torques acting on the aerial vehicle. The estimated external forces and/or torques are continually monitored while the aerial vehicle is landing to determine when the aerial vehicle is sufficiently supported by a landing surface. In some embodiments, semantic information associated with objects in the environment is utilized to configure parameters associated with the touchdown detection process.

Abstract: 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.

Abstract: A computer accesses a first symbolic expression for an output value as a function of an input value. The computer computes a first symbolic Jacobian of the input value with respect to an input tangent space from a symbolic Lie group definition. The computer computes a second symbolic Jacobian of the output value with respect to the input value. The computer computes a third symbolic Jacobian of an output tangent space with respect to the input value from the symbolic Lie group definition. The computer applies symbolic matrix multiplication to the first symbolic Jacobian, the second symbolic Jacobian, and the third symbolic 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.

Abstract: 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.

Abstract: A computer stores dense maps generated by one or more aerial vehicles. The computer generates a global graph based on the dense maps and a sparse map. The computer stores a representation of one or more paths traversed by the one or more aerial vehicles within the global graph. The computer determines a path from an origin location to a destination location based on the global graph. The determined path enables an aerial vehicle to avoid objects.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may determine a first acceleration of the UAV based at least on information from an onboard accelerometer received at least one of prior to or during takeoff. The UAV may determine a second acceleration of the UAV based at least on location information received via a satellite positioning system receiver at least one of prior to or during takeoff. The UAV may further determine a relative heading of the UAV based at least in part on the first acceleration and the second acceleration, and may be directed to navigate an environment based at least on the determined relative heading.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may receive location information via the global navigation satellite system (GNSS) receiver and may receive acceleration information via an onboard accelerometer. The UAV may determine a first measurement of acceleration of the UAV in a navigation frame of reference based on information from the accelerometer prior to or during takeoff. In addition, the UAV may determine a second measurement of acceleration of the UAV in a world frame of reference based on the location information received via the GNSS receiver prior to or during takeoff. The UAV may determine a relative heading of the UAV based on the first and second acceleration measurements. The determined relative heading may be used for navigation of the UAV at least one of during or after takeoff of the UAV.

Abstract: A technique is described for developing and using applications and skills with an autonomous vehicle. In an example embodiment, a development platform is provided that enables access to a developer console for developing software modules for use with an autonomous vehicle. Using the developer console, a developer user can specify instructions for causing an autonomous vehicle to perform one or more operations. For example, to control the behavior of an autonomous vehicle, the instructions can cause an executing computer system at the autonomous vehicle to generate calls to an application programming interface (API) associated with an autonomous navigation system of autonomous vehicle. Such calls to the API can be configured to adjust a parameter of a behavioral objective associated with a trajectory generation process performed by the autonomous navigation system that controls the behavior of the autonomous vehicle.

Abstract: Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.