A method for determining situational awareness in a worksite includes setting at least one environment modelling apparatus (EM) at least one of: on a machine or external from the machine; setting at least one tracking apparatus (TA) at least one of: on the machine or external from the machine; acquiring data by the at least one tracking apparatus (TA); and acquiring data by the at least one environment modelling apparatus. Further, the method includes receiving, by at least one position determination unit (PDU), data related to the at least one tracking apparatus (TA) and data related to the at least one environment modelling apparatus (EM); and determining, by the at least one position determination unit (PDU), based at least in part on the received data, the location and orientation of the machine in the worksite.

An apparatus for evaluating a quality for image capture comprises a stored (101) for storing a model of a scene and a capture circuit (105) for generating virtual captured images for a camera configuration by rendering from the model. A depth generation circuit (107) generates model depth data from the model and a depth estimation circuit (111) generates estimated depth data from the virtual captured images. A first synthesis circuit (109) and a second synthesis circuit (113) generates first and second view images for test poses by processing the virtual captured images based on the model depth data or estimated depth data respectively. A reference circuit (103) generates reference images for the f test poses by rendering based on the model. A quality circuit (115) generates a quality metric based on a comparison of the first view images, the second view images, and the reference images.

A feature mapping computer system configured to (i) receive a first localized image including a first photo and a first location; (ii) receive a second localized image including a second photo and a second location; (iii) identify a roadway feature depicted in both the first and second photos; (iv) generate, using a photogrammetry module, a point cloud based upon the first and second photos and first and second locations; (v) generate a localized point cloud by assigning a location to the point cloud based upon at least one of the first and second locations; and (vi) generate an enhanced base map that includes a roadway feature by embedding an indication of the identified roadway feature onto the localized point cloud.

The disclosure presents novel methods to conduct aerial surveying, inspection and measurements with higher accuracy in a fast and easy way, comprising: (1) flying a drone with an accelerometer, gyro, and camera sensors over a target object; (2) capturing a first aerial image at a first position; (3) capturing a second aerial image at a second position, wherein the second position has a horizontal and vertical displacement from the first position; (4) calculating the displacements between the first and second location using a sensor fusion estimation algorithm from the position sensors' data; (5) solving for the pixel depth information of the aerial images by using a single-camera stereophotogrammetry algorithm with the relative altitude and horizontal distance; (6) deriving the ground sample distance (GSD) of each pixel from the calculated depth information; (7) using the image pixel GSD values to compute any geometric properties of the target objects.

A computer implemented method includes obtaining data for raw image frames captured by a moving camera. The raw image frames are indexed geographically, and a graph is created from the multiple raw image frames. The graph includes image frames as vertices and edges that represent image frames having overlapping image information. The method further includes skipping frames based on the amount of overlap, determining a frame having an interesting feature, using the graph to find additional raw image frames that have the interesting feature, combining multiple raw image frames to form a unique image frame, and transmitting the unique image frame.

A handheld device for the image-based measurement of a remote object, comprising a housing having a front side and a rear side, a first and second camera, which are arranged having a stereo base on the rear side, for recording images of the object, an analysis unit having an algorithm for the stereophotogrammetric analysis of the images of the cameras and a display unit, which is arranged on the front side, for displaying images of the object and results of the stereophotogrammetric analysis, wherein the housing has a longitudinal axis, the stereo base is aligned diagonally relative to the longitudinal axis, and the analysis unit is designed for the purpose of taking into consideration the relative alignment of the stereo base during the stereophotogrammetric analysis.

A handheld device for the image-based measurement of a remote object, comprising a housing having a front side and a rear side, a first and second camera, which are arranged having a stereo base on the rear side, for recording images of the object, an analysis unit having an algorithm for the stereophotogrammetric analysis of the images of the cameras and a display unit, which is arranged on the front side, for displaying images of the object and results of the stereophotogrammetric analysis, wherein the housing has a longitudinal axis, the stereo base is aligned diagonally relative to the longitudinal axis, and the analysis unit is designed for the purpose of taking into consideration the relative alignment of the stereo base during the stereophotogrammetric analysis.

Computer-implemented methods, systems, and program products are provided that assist in aspects of aerial surveying, including selective display of planned flight path segments, marking of ground conditions, monitoring coverage of a planned flight path, and providing guidance information for aircraft navigation, including speed and turns.

Unmanned aerial vehicles (UAVs) may facilitate insurance-related tasks. UAVs may actively be dispatched to an area surrounding a property, and collect data related to property. A location for an inspection of a property to be conducted by a UAV may be received, and one or more images depicting a view of the location may be displayed via a user interface. Additionally, a geofence boundary may be determined based on an area corresponding to a property boundary, where the geofence boundary represents a geospatial boundary in which to limit flight of the UAV. Furthermore, a navigation route may be determined which corresponds to the geofence boundary for inspection of the property by the UAV, the navigation route having waypoints, each waypoint indicating a location for the UAV to obtain drone data. The UAV may be directed around the property using the determined navigation route.

Unmanned aerial vehicles (UAVs) may facilitate insurance-related tasks. UAVs may actively be dispatched to an area surrounding a property, and collect data related to property. A location for an inspection of a property to be conducted by a UAV may be received, and one or more images depicting a view of the location may be displayed via a user interface. Additionally, a geofence boundary may be determined based on an area corresponding to a property boundary, where the geofence boundary represents a geospatial boundary in which to limit flight of the UAV. Furthermore, a navigation route may be determined which corresponds to the geofence boundary for inspection of the property by the UAV, the navigation route having waypoints, each waypoint indicating a location for the UAV to obtain drone data. The UAV may be directed around the property using the determined navigation route.