A method for alignment a lidar with a camera of a vehicle includes: aggregating multiple lidar scans performed by the lidar of a vehicle while the vehicle is in motion to generate an aggregated point-cloud; rendering the aggregate point-cloud onto a camera image to generate a rendered image; comparing the rendered image with the camera image to determine a difference between the rendered image and the camera image, wherein a difference value is indicative of the difference between the rendered image and the camera image is represented; and determining that the camera is aligned with the lidar in response to determining that the difference value is less than or equal to a predetermined threshold.

A device for measuring shoreline distance and distance to objects on a boat, including a measurement mechanism for measuring shoreline distance and object distance including a global positioning system (GPS) in combination with a camera and sensor. A method of determining shoreline distance and distance to objects on a boat, by actuating the device, measuring the shoreline distance and object distance from the boat, and displaying the shoreline distance and object distance on the graphical interface display.

Location may be determined in a way that does not primarily rely on jam-able or spoof-able techniques. For example, an apparatus may have multiple location-determining units, each unit having a different level of trust. One unit may have a first (e.g., highest) level of trust, a second such unit may have a second (e.g., medium) level of trust lower than the first level of trust, and a third such unit may have a third (e.g., lowest) level of trust lower than the first and second levels of trust. The apparatus may generally prefer to determine its location using the highest level of trust unit that is available at any given time.

Techniques provided herein are directed toward virtually extending an updated set of output positions of a mobile device determined by a VIO by combining a current set of VIO output positions with one or more previous sets of VIO output positions in such a way that ensure all outputs positions among the various combined sets of output positions are consistent. The combined sets can be used for accurate position determination of the mobile device. Moreover, the position determination further may be based on GNSS measurements.

A system and method determine the location of a device. The device collects sensor data using one or more sensors. Based on the sensor data, one or more localization models are selected from a plurality of localization models. The selected models are applied to generate one or more candidate locations. The current location of the device is determined based on the one or more candidate locations.

A surveying pole is part of a primary surveying system (e.g., a Global Navigation Satellite System (GNSS) or a total station). Cameras are mounted to the surveying pole and used for ground tracking as the survey pole is moved from a place where the primary surveying system is unimpeded to an environment where the primary surveying system is impaired (e.g., to a GNSS-impaired environment or to a position that is blocked from view of the total station). Using ground tracking and/or other sensors, surveying can be continued even though the primary surveying system is impaired.

In general, platforms, systems, and methods are provided for identifying and privacy protecting authorized persons, vehicles, and objects in motion in the field of view of a video security camera or set of networked security cameras for use in a residential or business community. Various embodiments incorporate a security identification system that includes one or more mobile devices such as a smartphone or smartwatch, one or more cameras situated within a community with processors connected to data centers via the internet, and software running a cloud service infrastructure communicating to both the smartphone or smartwatch. The system can also include the ability to translate locational information from a smartphone or smartwatch into corresponding identification and geolocation information on images from security cameras.

One embodiment of the present invention relates to a method for a reader to transmit and receive a signal to and from a tag, the method comprising: a step in which a first reader transmits a first signal to a tag; and a step in which the first reader receives the first signal reflected on the tag. The timing for the first reader to transmit the first signal is determined by using an offset value based on an ID related to the first reader.

A positioning method includes: acquiring the credibility of each positioning subsystem in different states and generating a credibility data table; acquiring the real-time credibility from the corresponding credibility data table according to real-time positioning data of each positioning subsystem; calculating a first information distribution weight coefficient of each positioning subsystem involving a fusion operation of an filter according to the real-time credibility of each positioning subsystem; respectively feeding back, by a main filter, a second information distribution weight coefficient of each positioning subsystem involving the fusion operation to each sub-filter according to global data; determining a final information distribution weight coefficient of each positioning subsystem involving the fusion operation according to the first information distribution weight coefficient and the second information distribution weight coefficient; and performing, by the filter, the fusion operation according to the final information distribution weight coefficient of each positioning subsystem and outputting a final positioning result.

A LIDAR-to-vehicle alignment system includes a memory and an autonomous driving module. The memory stores points of data provided based on an output of a LIDAR sensor and GPS locations. The autonomous driving module performs an alignment process including performing feature extraction on the points of data to detect one or more features of one or more predetermined types of objects having one or more predetermined characteristics. The features are determined to correspond to one or more targets because the features have the predetermined characteristics. One or more of the GPS locations are of the targets. The alignment process further includes: determining ground-truth positions of the features; correcting the GPS locations based on the ground-truth positions; calculating a LIDAR-to-vehicle transform based on the corrected GPS locations; and based on results of the alignment process, determining whether one or more alignment conditions are satisfied.