An unmanned autonomous vehicle (aerial or ground) assessment and reporting system may conduct micro scans of interior portions of a structure, such as walls, windows, doorways, stairs, and the like. Scan data from one or more sensor types may be compared with stored profile data from a library of profiles using computer vision or other matching techniques to identify characteristics, defects, damage, construction materials, etc. An adaptive response system may modify the types of sensors used for scanning and/or the scanning pattern itself based on matched profile data. Modifications to the scanning process are implemented in real-time based on identified characteristics of the interior portions of the structure.

Unmanned vehicles can be terrestrial, aerial, nautical, or multi-mode. Unmanned vehicles may be used to survey a property in response to or in anticipation of damage to the property. For example, an unmanned vehicle may analyze information about the property and based on the information provide graphics and information associated with the surveying of the property.

A robot includes an image sensor that captures the environment of a storage site. The robot visually recognizes regularly shaped structures to navigate through the storage site using various object detection and image segmentation techniques. In response to receiving a target location in the storage site, the robot moves to the target location along a path. The robot receives the images as the robot moves along the path. The robot analyzes the images captured by the image sensor to determine the current location of the robot in the path by tracking a number of regularly shaped structures in the storage site passed by the robot. The regularly shaped structures may be racks, horizontal bars of the racks, and vertical bars of the racks. The robot can identify the target location by counting the number of rows and columns that the robot has passed.

A robot includes an image sensor that captures the environment of a storage site. The robot visually recognizes regularly shaped structures to navigate through the storage site using various object detection and image segmentation techniques. In response to receiving a target location in the storage site, the robot moves to the target location along a path. The robot receives the images as the robot moves along the path. The robot analyzes the images captured by the image sensor to determine the current location of the robot in the path by tracking a number of regularly shaped structures in the storage site passed by the robot. The regularly shaped structures may be racks, horizontal bars of the racks, and vertical bars of the racks. The robot can identify the target location by counting the number of rows and columns that the robot has passed.

A long-distance sampling device for feces in a poultry house is provided. The device includes an unmanned aerial vehicle body including a wireless controller, supporting feet arranged on the unmanned aerial vehicle body, and a sampling unit arranged on the unmanned aerial vehicle body for collecting fecal samples. The sampling unit further includes a sampling power portion arranged in the unmanned aerial vehicle body and a sampling portion arranged on the unmanned aerial vehicle body. The sampling portion further includes a cylindrical sampling tube and a sampling shaft rotatably connected to the sampling tube, spiral blades are fixedly connected to the sampling shaft, and the sampling power portion drives the sampling shaft to rotate. According to the device, it is convenient for workers to take samples in the poultry house, thereby the convenience of sampling is improved, and the workers do not need to get into the breeding areas.

Exemplary embodiments relate to an indoor drone system including an autonomous drone configured for autonomous navigation, and a computing system in communication with the autonomous drone. The autonomous drone includes an optical code reader and at least one navigational sensor. The computing system includes a verification module.

The subject matter described herein includes a modular and extensible approach to integrate noisy measurements from multiple heterogeneous sensors that yield either absolute or relative observations at different and varying time intervals, and to provide smooth and globally consistent estimates of position in real time for autonomous flight. We describe the development of the algorithms and software architecture for a new 1.9 kg MAV platform equipped with an IMU, laser scanner, stereo cameras, pressure altimeter, magnetometer, and a GPS receiver, in which the state estimation and control are performed onboard on an Intel NUC 3.sup.rd generation i3 processor. We illustrate the robustness of our framework in large-scale, indoor-outdoor autonomous aerial navigation experiments involving traversals of over 440 meters at average speeds of 1.5 m/s with winds around 10 mph while entering and exiting buildings.

An aerial system, preferably including one or more proximity sensors, such as sensors arranged in opposing directions. A method for aerial system operation, preferably including: determining a set of sensors; sampling measurements at the set of sensors; localizing the aerial system based on the measurements, such as to determine one or more obstacle clearances; and controlling system flight, such as based on the clearances.

An example customer assistance system and associated methods are described. The example customer assistance system includes a shopping cart, a customer assistance unit mounted on the shopping cart, and a location system disposed remotely from the shopping cart and the customer assistance unit. The customer assistance unit includes a light source oriented upwards relative to horizontal, and an actuator for actuating the light source into an illumination position. In the illumination position, the light source projects a light beam over the shopping cart. The locating system includes at least one image capturing device configured to scan a ceiling for the light beam and to determine a source location of the shopping cart based on a ceiling location of the light beam.

A system for remotely displaying video captured by an unmanned aerial system (UAS), the system comprising an unmanned aerial system (UAS) including an unmanned aerial vehicle (UAV), one or more image capture devices coupled to the UAV for capturing video of an environment surrounding the UAV, and an onboard transmitter for transmitting a short-range or medium-range wireless signal carrying the video of the environment surrounding the UAV; a portable communications system including a receiver for receiving the short-range or medium-range wireless signal transmitted from the UAS and a transmitter for transmitting a long-range wireless signal carrying the video of the environment surrounding the UAV to a wide area network (WAN); and a server in communication with the WAN, the server being configured to share the video of the environment surrounding the UAV with one or more remote devices for display on the one or more remote devices.