A surveying instrument comprises a monopod installed at a reference point, a surveying instrument main body provided at the monopod and having an attitude detector, wherein the surveying instrument main body carries out an image pickup and a scanning of an object to be measured at a pre-movement and a post-movement respectively, obtains at least three cross points of a locus of a scan pattern of the pre-movement and the post-movement, and calculates three-dimensional coordinates of an installation point of the post-movement based on three-dimensional coordinates of the cross points measured from the reference point, a measurement result of the cross points measured from the installation point of the post-movement and a detection result of the attitude detector.

An aerial drone system configured to serve as personal drone assistance is disclosed. The drone assistant is configured to follow the user and provide (1) audiovisual output including video, audio, and navigation, (2) environmental comfort including shade, light, misters for the benefit of the user, as well as (3) privacy and security. To provide audiovisual output, the drone is configured to track the user and maintain a constant height and distance relative to the user, preferably a few feet away in front of the user. To provide environmental comfort including shade, for example, the drone is configured to automatically maintain a position between the user and the sun, thus causing a shadow to be continually cast on the user. This shading is enhanced by specialized louvres and screens configured to prevent any direct sunlight from directly impinging on the user.

Methods and apparatus to count people are disclosed. Example apparatus disclosed herein include means for discarding existing characteristic datasets from a list; means for populating the list with first characteristic datasets obtained from a first plurality of images representative of an environment during a first period of time of a media presentation; means for limiting a number of the first characteristic datasets stored in the list for a first location represented in the first plurality of images; and means for comparing the first characteristic datasets to each other to determine a first number of unique faces in the environment during the first period of time, wherein the means for discarding is to delete the first characteristic datasets from the list, and the means for populating is to re-populate the list with second characteristic datasets obtained from a second plurality of images representative of the environment.

An image correction method includes: acquiring band images obtained by imaging a subject, and a high-resolution image having a resolution higher than that of the band images; acquiring a position difference between the object band image and the reference band image among the band images; by using a pixel of the object band image as an object pixel, for each object pixel, determining a pixel value of each sub-region obtained by dividing the imaging region of the object pixel into a plurality of regions, based on the pixel value of the object pixel and a relationship between pixel values of the pixels of the high-resolution image corresponding to the object pixel; and creating a corrected band image that holds a pixel value of light on the object band image at the pixel position of the reference band image, from the determined pixel value of each sub-region and the position difference.

A technique enables a surveying device to acquire a direction of a target by using as little extra hardware as possible. A method aims a laser scanner, as a surveying device, at a reflective prism that is a target. The method includes obtaining a photographic image of the laser scanner that is captured by a smartphone from the reflective prism side and determining a direction of the reflective prism as seen from the laser scanner on the basis of the photographic image. The method also includes rotating the laser scanner to make the laser scanner face the reflective prism straight on, on the basis of the direction of the reflective prism as seen from the laser scanner.

Systems and methods for providing an accurate determination of above ground level (AGL) altitude for use in precision agriculture. A 3D computer model of an agricultural field is generated by analyzing frames of a video of the agricultural field that have been obtained by a camera on an unmanned aerial vehicle flown over the agricultural field and generating 3D points that are used to create the 3D computer model. The 3D computer model is then scaled by fusing each 3D point with navigation sensor data associated with each frame. An AGL altitude for each 3D point of the scaled 3D computer model can then be determined. In another embodiment, a trajectory of the unmanned aerial vehicle can be determined and used to generate a georeferenced map using images captured by the camera.

A measuring system for measuring articles of clothing, comprising: a measuring table which has a table surface on which the article of clothing to be measured is laid; at least one laser distance meter which emits a laser beam, the laser distance meter being arranged in such a way that the laser beam extends in parallel with the table surface 4 at a short distance so that, when an article of clothing lies on the measuring table, the laser beam hits the article of clothing; at least one marking on which the article of clothing is laid before a measurement is carried out by means of the at least one laser distance meter; and a data-processing device which determines a length dimension of the article of clothing using the position of the at least one marking and using a value measured by the one or more laser distance meters.

Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.

A three-dimensional surface of the upper cover of the crops is recognized and recorded by a contactless scanning from above the field; a referential height h of the crop's stem is determined and the three-dimensional surface of the field from which the crops grow is determined. A reached height x of the vegetation for the individual points is computed by comparison of the three-dimensional surface of the upper cover of the crops with the three-dimensional surface of the field, whereby in case the reached height x of the vegetation is smaller than the referential height h of the crop's stem this difference between the reached height x of the vegetation and the referential height h of the stem determines in a given point of the field the angle β of lodging pursuant to the goniometric function. A classification of the grains into classes depending on the angle α of the slope of the lodged grain in interval 0° to 90° is realized, whereby this results in computation of the heights (h) of the grain spikes pursuant to relation x.sub.(0 to n)=h.Math.sin α.sub.(0 to n) for the creation of the digital vector map with the customizable levels of the lodging of the grains.

A structural analysis computing device may generate a proposed insurance claim for an object pictured in a three-dimensional (3D) image. 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. The processor may also determine a nature and an extent of damage to a damaged feature of the object, and a cost of repair based upon the nature and extent of the damage—such as by inputting the 3D image into a trained machine learning or pattern recognition program. The processor may generate a proposed claim form including the cost of repair, and display the claim form to a user for their review and/or approval.