Abstract: A technique for user interaction with an autonomous unmanned aerial vehicle (UAV) is described. In an example embodiment, perception inputs from one or more sensor devices are processed to build a shared virtual environment that is representative of a physical environment. The sensor devices used to generate perception inputs can include image capture devices onboard an autonomous aerial vehicle that is in flight through the physical environment. The shared virtual environment can provide a continually updated representation of the physical environment which is accessible to multiple network-connected devices, including multiple UAVs and multiple mobile computing devices. The shared virtual environment can be used, for example, to display visual augmentations at network-connected user devices and guide autonomous navigation by the UAV.

Abstract: A graphical user interface (GUI) for controlling the flight of an aircraft such as an unmanned aerial vehicle (UAV) is described. In example embodiments, the GUI includes a view of a physical environment from the perspective of the aircraft as well as various interactive elements through which a user can interact. The interactive elements presented in the GUI and the way in which user interaction with such elements is interpreted can depend on user selection from multiple available control modes. In some embodiments, user interaction with the GUI is interpreted based on a selected control mode and translated into behavioral objectives that are processed, along with other behavioral objectives, by a motion planner to maneuver the aircraft.

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 technique is introduced for autonomous landing by an aerial vehicle. In some embodiments, the introduced technique includes processing a sensor data such as images captured by onboard cameras to generate a ground map comprising multiple cells. A suitable footprint, comprising a subset of the multiple cells in the ground map that satisfy one or more landing criteria, is selected and control commands are generated to cause the aerial vehicle to autonomously land on an area corresponding to the footprint. In some embodiments, the introduced technique involves a geometric smart landing process to select a relatively flat area on the ground for landing. In some embodiments, the introduced technique involves a semantic smart landing process where semantic information regarding detected objects is incorporated into the ground map.

Abstract: Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.

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.