System, methods, and other embodiments described herein relate to estimating depth using a machine learning (ML) model. In one embodiment, a method includes acquiring image data according to criteria from a detector that uses a lens to resolve multiple angles of light per section of the detector. The method also includes mapping a kernel to the image data according to a view associated with the section and a size of the kernel. The method also includes processing the image data using the ML model to produce the depth according to the size of the kernel.

Systems and methods for image-based perception. The methods comprise: obtaining, by a computing device, images captured by a plurality of cameras with overlapping fields of view; generating, by the computing device, spatial feature maps indicating locations of features in the images; defining, by the computing device, predicted cuboids at each location of an object in the images based on the spatial feature maps; and assigning, by the computing device, at least two cuboids of said predicted cuboids to a given object when predictions from images captured by separate cameras of the plurality of cameras should be associated with a same detected object.

A method, for detecting an artefact in a stitched image, comprises: acquiring component images of an environment from respective vehicle mounted cameras with overlapping fields of view; forming (410) a stitched image from the component images; processing (420) at least a portion of the stitched image corresponding to the overlapping field of view with a classifier to provide a list of detected objects from the environment at respective locations in the stitched image; determining (430) whether any detected object in the list of detected objects is a duplicate of another object in the list of detected objects; and reporting any objects that are determined to be duplicates.

In a method and system for measuring a height map of a surface of an object, the following steps are carried out. Height maps of different sections of the surface of the object are measured, using an optical profilometer having a field of view covering an individual section, wherein each height map comprises height data. The measured height maps are grouped into different sets of height maps, wherein within each set each one of the height maps of the set has a valid overlap to at least one other height map of the set, and wherein each height map belongs to one set and does not have a valid overlap with any height map of another set. Within each set, the measured height maps are stitched to a sub-composite stitched height map. The sub-composite stitched height maps are combined to a composite height map.

The present disclosure discloses a real-time ranging method of a common-virtual-axis three-dimensional (3D) camera based on YOLOv3-tiny, including: acquiring, by the common-virtual-axis 3D camera, a far image and a near image on a same optical axis; processing image data of the near image and image data of the far image by using a YOLOv3-tiny target-recognition neural-network acceleration algorithm; determining, according to the processed image data, two target recognition frames corresponding to a preset recognized object on the far image and the near image; performing curvature scale space (CSS) corner recognition on image data in the target recognition frames; obtaining an average value of distances from far and near corners to a center point of the image data in each of the target recognition frames according to recognized corner coordinates; and substituting the obtained average value into an optical relation of the common-virtual-axis 3D camera, to obtain distance information.

An image generation apparatus includes an objective optical system, an optical-path splitter, an image sensor, and an image processor. Both a first imaging area and a second imaging area are images of a field of view of the objective optical system. An outer edge of the first imaging area and an outer edge of the second imaging area are located at an inner side of a predetermined image pickup area, and an image of the outer edge located in the predetermined image pickup area is captured by the image sensor. The image processor has reference data, and in the image processor, a feature point is extracted on the basis of the imaging area. An amount of shift between the predetermined imaging area and the image sensor is calculated from the feature point and the reference data.

One aspect of the invention relates to a fully automatic method for calculating the current, geo-referenced position and alignment of a terrestrial scan-surveying device in situ on the basis of a current panoramic image recorded by the surveying device and at least one stored, geo-referenced 3D scan panoramic image.

A system for moving an object within an environment, wherein the system includes at least one modular wheel configured to move the object. The modular wheel includes a body configured to be attached to the object, a wheel, a drive configured to rotate the wheel and a controller configured to control the drive. One or more processing devices configured are provided to receive an image stream including a plurality of captured images from each of a plurality of imaging devices, the plurality of imaging devices being configured to capture images of the object within the environment, analyse the images to determine an object location within the environment, generate control instructions at least in part using the determined object location and provide the control instructions to the controller, the controller being responsive to the control instructions to control the drive and thereby move the object.

A method for reconstructing a set of one or more MRI images from one or more segmented acquisitions of MRI raw data includes, for each respective shot capturing a part of the MRI raw data of the respective images, capturing and reconstructing a low-resolution navigation image of a region of interest, either from the part of the MRI raw data or from an additional MRI raw data acquired adjacent to the part of the MRI raw data in time. Each segmented acquisition consists of a number of shots.

A surveillance system includes fixed video cameras and mobile security devices. Each mobile security device includes a mobile video camera, a memory, a transceiver and a controller that is configured to receive instructions from a surveillance system controller to fly to a particular location at which an incident is believed to be occurring and to instruct the mobile video camera to capture video of the incident. In some cases, the controller of the mobile security device is configured to solicit and receive one or more video streams from fixed video cameras that are determined to meet predetermined criteria with respect to the incident, and store the one or more received video streams in the memory. The controller of the mobile security device may subsequently transmit the collected video streams to the surveillance system controller.