An autonomous base station for unmanned aerial vehicles (‘UAVs’) is disclosed, which includes a landing surface for a UAV, configured with at least one power transfer bus for supplying power to a power source of a UAV thereon. The base station further includes a networking module and data processing means operably connected to, and configured to control, the power transfer bus and the networking module. The data processing means is operably connected to the UAV through the networking module, and further configured to receive, store and process data from the UAV or another. The base station further includes a power supply operably connected to the or each power transfer bus, the or each networking module and the data processing means. A network of at least two such base stations is also disclosed, for sensing, modelling and monitoring an environment with UAVs.

This application discloses a calibration method for navigation of an unmanned aerial vehicle (UAV), a non-transitory computer-readable storage medium and a UAV implementing the same. The calibration method includes: collecting, during a flight of the UAV, reference data during two measurements of a reference vector performed by a vector sensor; acquiring a zero-point offset M.sub.0 of the vector sensor according to the reference data; acquiring original data R.sub.k of any vector measured by the vector sensor; acquiring valid data V.sub.k according to the zero-point offset M.sub.0 and the original data R.sub.k; and control headings and postures of the UAV according to the valid data V.sub.k. With the calibration method in this application, the valid data V.sub.k is defined as a vector data acquired after a zero-point error of the original data R.sub.k is eliminated, which is more closely approximated to an actual value of a to-be-measured vector.

This application discloses a calibration method for navigation of an UAV including a vector sensor. The calibration method includes: collecting, during a flight of the UAV, a current correction value and current data during a current measurement performed by the vector sensor; acquiring previous data during a previous measurement performed by the vector sensor; acquiring an adjustment quantity according to the current data and the previous data; acquiring a next correction value according to the current correction value and the adjustment quantity; and acquiring next original data during a next measurement performed by the vector sensor, acquiring next valid data according to the next original data and the next correction value , and controlling headings and postures of the UAV according to the next valid data. With the calibration method of this application, the next valid data V.sub.k+1 more closely approximated to a true value can be obtained.

An improved UAV system and methods for operation in an inventory management system. The methods include generating a three dimensional (3D) map and estimating a position and orientation of the UAV based upon this map; autonomously navigating the UAV in the environment by using the generated 3d map in conjunction with the position and the orientation of the UAV; performing static and dynamic obstacle avoidance in the environment using collision avoidance; and finding the optimal path from a source node to a destination node within the environment.

A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.

A digital compass with two or more multi-axis magnetometers and a processing element to determine a heading and detect any offset error in the heading is described. One electronic device includes first and second magnetometers. The second magnetometer can be disposed at least a specified distance or co-located and offset at least a specified angle from the first magnetometer. A processing device determines a magnetic field at the electronic device using a first output from the first magnetometer, detects an offset error in the magnetic field using a second output from the second magnetometer, and reports the offset error in the magnetic field.

Methods and systems for automatically deploying an autonomous drone from a vehicle in response to a triggering event or accident so that data associated with the triggering event or accident may be automatically obtained are described. In one embodiment, a method for deploying an autonomous drone in response to a triggering event is described. The method includes providing an autonomous drone in a vehicle. The method also includes detecting a triggering event associated with the vehicle. Upon detection of the triggering event, the method includes automatically deploying the autonomous drone from the vehicle. The method further includes implementing, by the autonomous drone, a plurality of automatic actions, including recording data associated with the vehicle in which the autonomous drone is provided.

A method for synchronizing virtual and real statuses of a digital twin system of an unmanned aerial vehicle (UAV) includes: performing parameter configuration for a virtual object system and a physical object system of the UAV; performing time synchronization between the virtual object system and the physical object system; detecting an event trigger type, wherein the event trigger type is a training event or a monitoring event; and triggering a corresponding synchronization controller based on the detected event trigger type, such that the synchronization controller performs result synchronization and process synchronization for the virtual object system and the physical object system based on the event trigger type, where a synchronization controller corresponding to the training event is a controller for synchronizing a physical object to a virtual object, and a synchronization controller corresponding to the monitoring event is a controller for synchronizing the virtual object to the physical object.

Techniques and systems are described for generating and using a sound localization model. A described technique includes obtaining for a building a sound sensor map indicating locations of first and second sound sensor devices in respective first and second rooms of the building; causing an autonomous device to navigate to the first room and to emit, during a time window, sound patterns at one or more frequencies within the first room; receiving sound data including first and second sound data respectively from the first and second sound sensor devices that are observed during the time window; and generating and storing a sound localization model based on the sound sensor map, autonomous device location information, and the received sound data, the model being configured to compensate for how sounds travels among rooms in at least a portion of the building such that an origin room of a sound source is identifiable.

The application relates to an aircraft, a method, an apparatus and a computer readable storage medium for controlling the aircraft with at least one sensor arranged thereon, the method including detecting a motor state of the aircraft, acquiring at least one sensing data of the at least one sensor, and controlling the aircraft to perform a startup operation or a shutdown operation according to the motor state and the at least one sensing data, so that the aircraft can be autonomously controlled to perform the startup operation or the shutdown operation, and the user experience is improved.