A structural analysis computing device may generate a proposed insurance claim and/or generate a proposed insurance quote for an object pictured in a three-dimensional (3D) image. The structural analysis computing device may be coupled to a drone configured to capture exterior images of the object. The structural analysis computing device may include a memory, a user interface, an object sensor configured to capture the 3D image, and a processor in communication with the memory and the object sensor. The processor may access the 3D image including the object, and analyze the 3D images to identify features of the object—such as by inputting the 3D image into a trained machine learning or pattern recognition program. The processor may generate a proposed claim form for a damaged object and/or a proposed quote for an uninsured object, and display the form to a user for their review and/or approval.

A structural analysis computing device may generate a proposed insurance claim and/or generate a proposed insurance quote for an object pictured in a three-dimensional (3D) image. The structural analysis computing device may be coupled to a drone configured to capture exterior images of the object. The structural analysis computing device may include a memory, a user interface, an object sensor configured to capture the 3D image, and a processor in communication with the memory and the object sensor. The processor may access the 3D image including the object, and analyze the 3D images to identify features of the object—such as by inputting the 3D image into a trained machine learning or pattern recognition program. The processor may generate a proposed claim form for a damaged object and/or a proposed quote for an uninsured object, and display the form to a user for their review and/or approval.

An ortho-image creation method includes: first photographing of photographing a road; second photographing of photographing an area covered by the obstacle from an altitude lower than the obstacle by a second photographing apparatus, and obtaining a plurality of second photographed images; first coordinate acquisition of acquiring three-dimensional coordinates; second coordinate acquisition of acquiring three-dimensional coordinates of a second feature point located in the area covered by the obstacle and included in at least two of the plurality of second photographed images; and ortho-image creation of creating a corrected ortho-image obtained by correcting at least a part of the area covered by the obstacle in the road surface to an area not covered by the obstacle, on the basis of the plurality of first photographed images, the plurality of second photographed images, the three-dimensional coordinates of the first feature point, and the three-dimensional coordinates of the second feature point.

A three-dimensional information reconstraction device includes a corresponding point detector that detects a plurality of corresponding point pairs to which a first feature point included in a first image captured by a first image capturing device and a second feature point included in a second image captured by a second image capturing device correspond, and a three-dimensional coordinate deriver that, based on the plurality of corresponding point pairs, reconstructs three-dimensional coordinates to which the first feature point is inverse-projected.

The present invention relates to the efficient use of both local and remote computational resources and communication bandwidth to provide distributed environment mapping using a plurality of mobile sensor-equipped devices.

According to a first aspect, there is provided a method of determining a global position of one or more landmarks on a global map, the method comprising the steps of determining one or more differences between sequential sensor data captured by one or more moving devices; determining one or more relative localisation landmark positions with respect to the one or more moving devices; determining relative device poses based one or more differences between sequential sensor data relative to the one or more relative localisation landmark positions; and determining a correlation between each device pose and the one or more relative localisation landmarks positions.

The present invention relates to the efficient use of both local and remote computational resources and communication bandwidth to provide distributed environment mapping using a plurality of mobile sensor-equipped devices.

According to a first aspect, there is provided a method of determining a global position of one or more landmarks on a global map, the method comprising the steps of determining one or more differences between sequential sensor data captured by one or more moving devices; determining one or more relative localisation landmark positions with respect to the one or more moving devices; determining relative device poses based one or more differences between sequential sensor data relative to the one or more relative localisation landmark positions; and determining a correlation between each device pose and the one or more relative localisation landmarks positions.

An automatic range finding method is applied to measure a distance between a stereo camera and a reference plane. The automatic range finding method includes acquiring a disparity-map video by the stereo camera facing the reference plane, analyzing the disparity-map video to generate a depth histogram, selecting a pixel group having an amount greater than a threshold from the depth histogram, calculating the distance between the stereo camera and the reference plane by weight transformation of the pixel group, and applying a coarse-to-fine computation for the disparity-map video.

An automatic range finding method is applied to measure a distance between a stereo camera and a reference plane. The automatic range finding method includes acquiring a disparity-map video by the stereo camera facing the reference plane, analyzing the disparity-map video to generate a depth histogram, selecting a pixel group having an amount greater than a threshold from the depth histogram, calculating the distance between the stereo camera and the reference plane by weight transformation of the pixel group, and applying a coarse-to-fine computation for the disparity-map video.

An imaging system is provided for a vehicle. In one implementation, the imaging system includes an imaging module, a first camera coupled to the imaging module, a second camera coupled to the imaging module, and a mounting assembly configured to attach the imaging module to the vehicle such that the first and second camera face outward with respect to the vehicle. The first camera has a first field of view and a first optical axis, and the second camera has a second field of view and a second optical axis. The first optical axis crosses the second optical axis in at least one crossing point of a crossing plane. The first camera is focused a first horizontal distance beyond the crossing point of the crossing plane and the second camera is focused a second horizontal distance beyond the crossing point of the crossing plane.

The present disclosure pertains to a system for providing persistent mission data to a fleet of vehicles. In some implementations, the system receives (i) information related to a first vehicle's location, the altitude of the first vehicle, and the first vehicle's orientation from the one or more first sensors and (ii) imagery data from one or more second sensors disposed on the first vehicle, wherein the imagery data includes instantaneous imagery and previously recorded imagery. The system geolocates the imagery data based on the first vehicle's altitude and the first vehicle's orientation relative to the terrain. The system transmits one or both of the instantaneous imagery or the previously recorded imagery to a fleet of vehicles. The system effectuates presentation of the imagery data on a three dimensional topographical map of the terrain.