Disclosed are, according to various embodiments, a method for transmitting, by a user equipment (UE), a first collective perception message (CPM) in a wireless communication system supporting a sidelink, and an apparatus therefor. Disclosed are the method and the apparatus therefor, the method comprising the steps of: obtaining first object information on surrounding objects through a sensor; receiving a second CPM including second object information; and transmitting the first CPM including the first object information and location information for the UE, wherein the second CPM further includes information on a location reliability of the second object information, the position information of the UE is corrected by applying an offset, based on the first object information and the second object information being object information for the same object, and the offset is determined by applying a ratio between a first position reliability related to the position information of the UE and the second position reliability included in the second CPM to a distance between an object position based on the first object information and an object position based on the second object information.

A positioning system and a calibration method of an objection location are provided. The calibration method includes the following. Roadside location information of a roadside unit (RSU) is obtained. Object location information of one or more objects is obtained. The object location information is based on a satellite positioning system. An image identification result of the object or the RSU is determined according to images of one or more image capturing devices. The object location information of the object is calibrated according to the roadside location information and the image identification result. Accordingly, the accuracy of the location estimation may be improved.

Provided is a driver assistance system that may acquire more accurate position information of a vehicle without extracting landmark information or feature point information around the vehicle, the driver assistance system including: a global positioning system (GPS) module configured to acquire GPS information of the vehicle; a lidar configured to acquire point cloud data for an outside field of view of the vehicle; a communicator configured to receive a high definition map; and a controller comprising at least one processor configured to process the point cloud data, the high definition map, speed data and steering angle data, the speed data and the steering angle data being received through a vehicle communication network, wherein the controller is configured to generate dead reckoning information in response to processing the speed data and the steering angle data, generate position information of the vehicle based on the dead reckoning information and the GPS information, acquire intensity data around the vehicle in response to processing the high definition map received based on the position information, and compare the intensity data and the point cloud data to correct the position information.

A method for determining a location of a vehicle includes a global navigation satellite system (GNSS) location system in a manufacturing environment. The method includes determining, when a key cycle transition condition of the vehicle and a vehicle gear transition condition of the vehicle are satisfied, a location parameter of the vehicle using an auxiliary location detection system, where the location parameter includes a location of the vehicle, identification information of the vehicle, and a timestamp of the vehicle. The method includes determining a vehicle time period based on the location parameter and a previous location parameter of the vehicle and validating a manufacturing routine of the vehicle when the location parameter satisfies a location condition and the vehicle time period satisfies a time condition.

A positioned location adjustment method and apparatus. The method includes: a first vehicle sends a request message to a plurality of reference vehicles, where the request message includes current location information of the first vehicle; the first vehicle receives a response message from the reference vehicle, where the response message includes positioned location information of the reference vehicle, a positioning error value of the reference vehicle, and vehicle identifier information of the reference vehicle; the first vehicle determines a second vehicle from the plurality of reference vehicles based on the positioning error value of the reference vehicle; and the first vehicle adjusts the first positioned location information based on positioned location information of the second vehicle and vehicle identifier information of the second vehicle, to obtain second positioned location information. According to the embodiments, positioning precision and accuracy can be improved.

An indoor positioning method based on image visual features. A Wi-Fi signal strength value of a Wi-Fi tag closest to a current location of a mobile device is matched with a signal strength list in a map database to obtain a first location of a first Wi-Fi tag with the greatest matching degree. A SURF descriptor of an image of the Wi-Fi tag closest to the current location of the mobile device is matched with SURF descriptors recorded in the signal strength list in the map database to discover an image of a Wi-Fi tag with the greatest matching degree, thereby obtaining a second location of a second Wi-Fi tag corresponding to the image of the Wi-Fi tag with the greatest matching degree. A three location of a three Wi-Fi tag is obtained according to a homography matrix corresponding to the image of the Wi-Fi tag with the largest matching degree and an empirical value of a positioning error. Positioning information of the mobile device is obtained according to the first location, the second location and the third location.

Techniques for calculating a geospatial position of a point of interest using a portable electronic device. A camera of the portable electronic device may observe the point of interest. An EDM device of the portable electronic device may capture a distance to the point of interest. An angle sensor may detect an orientation of the EDM device. A geospatial position of a GNSS receiver of the portable electronic device may be detected. A geospatial position of the EDM device may be calculated based on the geospatial position of the GNSS receiver. The geospatial position of the point of interest may be calculated based on the geospatial position of the EDM device, the orientation of the EDM device, and the distance to the point of interest.

The purpose of the present invention is to provide an information processing device that realizes more suitable ease of use. To accomplish the purpose, there is provided an information processing device provided with a video input unit for acquiring image data from an optical image, a signal receiver unit for acquiring a position computation signal, a communication unit for transmitting the image data, a first memory unit for storing position information pertaining to a place to which the image data is not transmitted, and a control unit for controlling the execution of image data transmission on the basis of the position information acquired from the position calculation signal and the position information stored in the first storage unit.

A system and method include generating environment data from skylight sensor data. The environment data includes a value of a geospatially dependent parameter associated with light received from a predetermined celestial light source. At least two of a compass direction of the predetermined celestial light source when the skylight sensor data was received, a time at which the skylight sensor data was received, or a geospatial coordinate at which the skylight sensor data was collected are received. At least one of the compass direction of the predetermined celestial light source when the skylight sensor data was received, the time at which the skylight sensor data was received, or the geospatial coordinate at which the skylight sensor data was collected is determined, at least in part, from the environment data.

A method of automatic labeling of images for supervised machine learning includes obtaining images of roadside objects with a camera mounted to a vehicle, recording a position and orientation of the vehicle within a defined coordinate system while obtaining the images recording position information for each roadside object with the same defined coordinates system as used while recording the position and orientation of the vehicle, and correlating a position of each of the obtained images of the roadside objects with the position information of each roadside object in view of the recorded position and orientation of the vehicle. The images are labeled to identify the roadside objects in view of the correlated position of each of the obtained images of the roadside objects.