A method for mapping an object buried in a subterranean surface includes receiving a first input data set. The first input data set is based on a GNSS signal, a SAR signal, and a GPR signal. The method further includes generating a raw image based on the first input data set, comparing the raw image to a calibration data set, identifying an object based on the raw image compared to the calibration data set, transmitting object data to a ground-based post-processing unit, and displaying the object data to a graphic user interface. The calibration data set may be based on material calibration data. The object data may be based on the object identified.

A method for mapping an object buried in a subterranean surface includes receiving a first input data set. The first input data set is based on a GNSS signal, a SAR signal, and a GPR signal. The method further includes generating a raw image based on the first input data set, comparing the raw image to a calibration data set, identifying an object based on the raw image compared to the calibration data set, transmitting object data to a ground-based post-processing unit, and displaying the object data to a graphic user interface. The calibration data set may be based on material calibration data. The object data may be based on the object identified.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for using a drone to test monitoring system sensors. In one aspect, the drone perform operations that include navigating from a first location of a property towards a second location of the property, detecting a sensor that is installed at the property, interrupting, by the drone, navigation through the property from the first location of the property towards the second location of the property to initiate a test of the detected sensor and (ii) determining a sensor test type for the detected sensor, administering a sensor test to the detected sensor, wherein the sensor test is based on the determined sensor test type, evaluating results of the administered test, storing data describing the results of the administered test, and continuing navigation through the property towards the second location of the property or towards another location of the property.

The invention discloses an unmanned aerial vehicle having multifunctional leg assembly and charging system, including unmanned aerial vehicle and charging station. The UAV includes obstacle avoidance sensors, flight control module, first signal processing module, electric undercarriage and power charge/storage module. The charging station includes power charge/supply module. The obstacle avoidance sensors sense obstacles near the UAV to generate obstacle sensing signals. The first signal processing module interprets and processes the obstacle sensing signals to determine whether there is an obstacle near the UAV, and when the judgment result is yes, an avoidance instruction is transmitted to the flight control module, so that the flight control module drives the UAV to avoid the obstacle. The electric undercarriage includes first leg frame, second leg frame and electric driving mechanism. The electric driving mechanism drives the first leg frame and the second leg frame to fold and unfold alternately. The power charge/storage module includes first positive electrode and first negative electrode. The charging station includes power charge/supply module. The power charge/supply module includes second positive electrode and second negative electrode. When the UAV parks on a platform of the charging station, and the first positive electrode and the first negative electrode are in contact with the second positive electrode and the second negative electrode, then the power charge/supply module charges electricity to the power charge/storage module.

A system for housing a drone for locating a source in an electrical structure includes a plurality of drones capable of hovering in positions to form a virtual enclosure around an electrical structure and a server communicably coupled to the drones. The virtual enclosure is divided into a plurality of cells. The drone is configured to measure a plurality of time difference of arrival (TDOA) values from signals originating from the source; calculate a plurality of propagation times comprising a propagation time for a calibration signal that travels from a drone to each of the plurality of cells; and send the TDOA values and the propagation times to a server. The server is configured to receive the TDOA values and the propagation times from the plurality of drones; and determine a location of the source based on the plurality of TDOA values and the plurality of propagation times.

Drone-based systems and methods are described for providing an airborne relocatable communication hub within a delivery vehicle for broadcast-enabled devices maintained within the delivery vehicle. Such a method has an aerial communication drone paired with the delivery vehicle transitioning to an active power state, uncoupling from a secured position on an internal docking station fixed within the delivery vehicle and then moving to a first deployed airborne position within the delivery vehicle. At a first position, the method has the aerial communication drone establishing a first wireless data communication path to a first broadcast-enabled device within the delivery vehicle, then establishing a second wireless data communication path to a second broadcast-enabled device within the delivery vehicle. The drone then couples the first and second wireless data communication paths it established operating as the airborne relocatable communication hub for the devices.

Structure inspections are performed with a high resolution by first performing a modeling flight path at a relatively high altitude over the structure. Images are gathered during the modeling flight path and are used to generate a three dimensional model of the structure. From the three dimensional model a lower altitude closer inspection flight path is defined and executed.

A platform-based observation system that is in communication with a substrate. The system is configured to identify a condition in, on, or within the substrate. The system has components selected from the group consisting of: inputs, processing, and outputs. The inputs may include a visual scanning sensor, an infrared scanning sensor, at least one GPS receiver, and a means for image collection. Processing includes the processing of measurements and image collection data to define conditions and organizing them according to file formatting associated with geographic systems. The outputs include recording the conditions and outputting the conditions on a monitor.

The present invention makes it possible for sounding of an inspection location on an outer wall of a building or the like to be performed using a simple operation. A user interface device accepts an input for designating an inspection location. The mobile robot device flies autonomously and moves to the inspection location, on the basis of the input into the user interface device and the current location of the mobile robot device. The mobile robot device inspects the inspection location using an inspection means such as a sounding means.

An automated image capturing and processing system and method may allow a field user to operate a UAV via a mobile computing device to capture images of a structure area of interest (AOI). The mobile computing device receives user and/or third party data and creates UAV control data and a flight plan. The mobile computing device executes a flight plan by issuing commands to the UAV's flight and camera controller that allows for complete coverage of the structure AOI. After data acquisition, the mobile computing device then transmits the UAV output data to a server for further processing. At the server, the UAV output data can be used for a three-dimensional reconstruction process. The server then generates a vector model from the images that precisely represents the dimensions of the structure. The server can then generate a report for inspection and construction estimation.