Embodiments of the present disclosure relate to a method and apparatus for generating a driving path. The method includes acquiring a two-dimensional image of a driving site obtained by a camera provided on an unmanned aerial vehicle through aerial photography, and a two-dimensional image of a site in front of a vehicle photographed by a camera provided on the vehicle; generating a global map based on the two-dimensional image of the driving site, and generating a local map based on the two-dimensional image of the site in front of the vehicle; and performing path planning based on the global map and the local map, to generate a global path and a local path, the local path following a direction of the global path.

A method for occupancy map synchronization in multi-vehicle networks may include receiving three-dimensional perception sensor (3DPS) data. The method may include generating a data message including an occupancy map (OM) octree. The OM octree may be generated from an occupancy map constructed from the 3DPS data. The method may include reducing at least one of a messaging time or a messaging size of the data message including the OM octree. The method may include transmitting the data message including the OM octree via a leader vehicle controller communicatively coupled to a leader vehicle. The method may include receiving the data message comprising the OM octree via a follower vehicle controller communicatively coupled to the follower vehicle. The method may include generating a local occupancy map from the data message comprising the OM octree via the follower vehicle controller.

Described is a method that involves operating an unmanned aerial vehicle (UAV) to begin a flight, where the UAV relies on a navigation system to navigate to a destination. During the flight, the method involves operating a camera to capture images of the UAV's environment, and analyzing the images to detect features in the environment. The method also involves establishing a correlation between features detected in different images, and using location information from the navigation system to localize a feature detected in different images. Further, the method involves generating a flight log that includes the localized feature. Also, the method involves detecting a failure involving the navigation system, and responsively operating the camera to capture a post-failure image. The method also involves identifying one or more features in the post-failure image, and determining a location of the UAV based on a relationship between an identified feature and a localized feature.

When an autonomous vehicle plans a new drive mission in a confined area, a first set of environmental information, and after a time interval, a second set of environmental information, are obtained by a device flying above a confined area including the departure point and the destination point of the vehicle, each set of environmental information including locations and geometries of objects in the confined area. Then, an object that is present at the same location in the first set of environmental information and in the second set of environmental information is classified as a semi-static object, and an object that is present at different locations in the first set of environmental information and in the second set of environmental information is classified as a dynamic object. A map including location and geometry of each object classified as a semi-static object is then created.

An imaging system can include a first and second camera configured to capture first and second sets of oblique images along first and second scan paths, respectively, on an object area. A drive is coupled to a scanning mirror structure, having at least one mirror surface, and configured to rotate the structure about a scan axis based on a scan angle. The first and second cameras each have an optical axis set at an oblique angle to the scan axis and include a respective lens to focus first and second imaging beams reflected from the mirror surface to an image sensor located in each of the cameras. The first and second imaging beams captured by their respective cameras can vary according to the scan angle. Each of the image sensors captures respective sets of oblique images by sampling the imaging beams at first and second values of the scan angle.

A method for generating map data comprising training a generator neural network by acquiring training data elements and training a generative adversarial network, comprising training a generator neural network to generate, for a satellite image and a road usage image of a training data element, the map data image of the training data element and comprising generating map data for a geographical region by acquiring road usage information specifying which parts of a geographical area have been used for driving a vehicle, acquiring a satellite image of the geographical area, forming a road usage image of the geographical area which has pixels, each pixel corresponding to a respective part of the geographical area and having a value indicating whether its part of the geographical area is specified by the road usage information to have been used; and feeding the satellite image and the road usage image to the trained generator neural network.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining that current data captured at a current location of a drone satisfies localization adjustment criteria; in response to determining that the current data captured at the current location of the drone satisfies the localization adjustment criteria, identifying previously captured image data; determining a previous expected location of the drone based on both an expected change in location of the drone and a first previous location determined from other image data captured before the previously captured image data; determining a location difference between the previous expected location of the drone and a second previous location determined from the previously captured image data; and determining the current location of the drone based on the location difference.

An approach is provided for location correction using feature point correspondences between images or image sources. The approach, for example, involves processing a first image and a second image to identify a feature that is visible in both the first image and the second image. The first image has an image-to-ground correspondence between the feature in the first image and a geographic location. The approach also involves transferring the image-to-ground correspondence from the first image to the second image. The approach further involves using the transferred image-to-ground correspondence to reference the feature in the second image to the geographic location.

In accordance with one embodiment, a method of locating mineral deposits suitable for production comprises obtaining via a computer an image of an area of land, determining from the image at least one fluid-expulsion structure present on the land, designating an area proximate the fluid-expulsion structure as a mineral exploration location; while in accordance with another embodiment a method of locating a hydrocarbon reservoir suitable for production is described which comprises obtaining via a computer an image of an area of land, determining from the image at least one fluid-expulsion structure present on the land, and designating an area proximate the fluid-expulsion structure as a hydrocarbon exploration location.

A method and system for determining positions on the ground from an aerial vehicle, including scanning the ground with a measuring beam to obtain a point cloud, wherein each point of the point cloud represents coordinates of a position on the ground in an aircraft-related coordinate system; segmenting a first subset of points within the point cloud, wherein the first subset of points shares a predefined first distortion class; segmenting a second subset of points within the point cloud, wherein the second subset of points shares a predefined second distortion class; geo-referencing the point cloud from the aircraft-related coordinate system to an Earth-related coordinate system; strip adjusting the geo-referenced first subset of points relative to the geo-referenced second subset of points for identifying a set of correction parameters; correcting the geo-referenced first subset of points by the set of correction parameters.