A networked computer system for recognizing objects in video images is described herein. The networked computer system includes a user display device, a camera, and an object recognition system. The camera includes an imaging device having a 360° field-of-view and a global positioning system (GPS) device. The object recognition system includes a processor programmed to execute an algorithm including receiving live-stream video images from the camera, detecting an object of interest within the live-stream video images, determining pixel coordinates associated with a center of the detected object of interest, receiving the geographic location data from the camera, determining a geographic location of the object of interest based on the determined pixel coordinates and the geographic location of the camera, and displaying the live-stream video images on the user display device including a visual indicator of the object of interest and the determined geographic location of the object of interest.

Provided are methods for semantic annotation of sensor data using unreliable map annotation inputs, which can include training a machine learning model to accept inputs including images representing sensor data for a geographic area and unreliable semantic annotations for the geographic area. The machine learning model can be trained against validated semantic annotations for the geographic area, such that subsequent to training, additional images representing sensor data and additional unreliable semantic annotations can be passed through the neural network to provide predicted semantic annotations for the additional images. Systems and computer program products are also provided.

A method for generating a feature-based localization map for a global navigation satellite system (GNSS) -based localization and/or a feature-based localization includes generating feature information for the feature-based localization map using at least one GNSS information, generating GNSS-related meta-information that allows inferences to be drawn about a GNSS situation on which the generation of the feature information was based, and assigning the generated GNSS-related meta-information to the generated feature information.

Methods and apparatus for interacting with a tag in a radio target area. More specifically, the present invention relates to methods and systems for monitoring temperature and other environmental conditions in a storage area and displaying environmental conditions as digital content in a user interactive interface based upon energy levels received from a radio target area and content from a sensor generating digital content.

Methods and apparatus for interacting with a tag in a radio target area. More specifically, the present invention relates to methods and systems for monitoring temperature and other environmental conditions in a storage area and displaying environmental conditions as digital content in a user interactive interface based upon energy levels received from a radio target area and content from a sensor generating digital content.

A method for compensating for the absence of GPS data during a period of GPS signal loss in determining travel mileage of a vehicle includes: detecting vehicle motion using an accelerometer during a period of time in which a GPS tracking device is unable to determine a location of the vehicle due to loss of GPS signal; determining a first location of the vehicle corresponding to the last known GPS location data point stored in memory; determining a second location of the vehicle corresponding to a point at which the GPS signal is reacquired; and calculating the distance between the first and second locations based on a straight-line distance calculation between the first and second locations, or based on the use of geospatial mapping data to plot a roadway route between the first and second locations.

Methods and systems are provided for navigating a mobile platform in an environment. A processor obtains information about an object in the environment, obtains information about a first satellite, and estimates a probability indicator for a non-line of sight signal transmission between a current satellite location of the first satellite and a current location of the mobile platform using the information about the first satellite and the information about the object. The processor further determines a discrepancy indicator using a movement information of the mobile platform and a movement information of the first satellite such that a weighting indicator can be determined using the estimated probability indicator and the determined discrepancy indicator. The processor then assigns a weighting indicator to a satellite signal transmitted from the first satellite in order to provide a first weighted signal for navigating the mobile platform.

An application running on a UE receives a command through a user interface or from a server to initiate the locating of a tracking device. The tracking device determines one or more hot spots it is close to. A server determines whether to instruct the tracking device to use its GNSS circuitry to determine its location based on a comparison of location information corresponding to the hot spot(s) and user selectable accuracy criteria. The UE may detect a beacon signal and determine an initial value corresponding to an initial signal strength of the beacon. The beacon may only broadcast for a predetermined period based on location scenario. The application produces a default initial indication, based on the initial signal strength and uses updated beacon signal strength values to adjust the indication relative to the default indication based on the updated signal strength value relative to the initial signal strength value

An application running on a UE receives a command through a user interface or from a server to initiate the locating of a tracking device. The tracking device determines one or more hot spots it is close to. A server determines whether to instruct the tracking device to use its GNSS circuitry to determine its location based on a comparison of location information corresponding to the hot spot(s) and user selectable accuracy criteria. The UE may detect a beacon signal and determine an initial value corresponding to an initial signal strength of the beacon. The beacon may only broadcast for a predetermined period based on location scenario. The application produces a default initial indication, based on the initial signal strength and uses updated beacon signal strength values to adjust the indication relative to the default indication based on the updated signal strength value relative to the initial signal strength value

A mobile electronic device including: a movement detection sensor; a positioning module; a processor; and a memory, wherein the processor determines whether the device is moving on the basis of a first tentative determination result obtained by determining whether the device is moving based on a value obtained from the movement detection sensor as well as a second tentative determination result obtained by determining whether the device is moving based on positional information detected by the positioning module, and, upon determining that the device is moving, stores the positional information detected by the positioning module in the memory.