A system and method for translating route guidance between a navigation system and a navigation service when different versions of a map database are being used. The navigation service applies a version translation table to identify differences between the map databases and translates route guidance accordingly before it is communicated to the navigation system.

The disclosure relates to a method for predicting and controlling robot walking. The method includes the following steps: constructing a grid map based on grid units marked with a status; establishing a dynamic detection model with a current location of a robot as a reference point based on the grid map; predicting a forward path condition of the robot based on the dynamic detection model; and controlling a walking mode of the robot based on the prediction result.

The measurement device acquires positional information of a measurement object stored in a storage unit, and acquires point group information of points indicating a surrounding feature acquired by an external sensor. Then, the measurement device calculates the positional information indicating a predetermined position of the measurement object existing in a predetermined range based on the point group information existing in the predetermined range, and outputs its reliability.

A system, a method and a computer program product are provided for determining building type of one or more buildings in a geographic region, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain a plurality of mobility features associated with the one or more buildings. The processor may be configured to determine, using a trained machine learning model, one or more transport modes for the one or more buildings, based on the plurality of mobility features. The processor may be further configured to determine, using the trained machine learning model, the building type of the one or more buildings based on the determined one or more transport modes.

A method for assigning a map attribute in an digital map for a vehicle. The method includes: ascertaining a validity range of the map attribute stored in absolute coordinates in relation to a reference line of the digital map; at least one base point of a defined geometry being ascertained originating from the map attribute using a defined segment of the reference line of the vehicle; and the map attribute being assigned to the defined segment intersected at the base point.

According to an aspect of an embodiment, operations may comprise obtaining a pose graph that comprises a plurality of nodes. The operations may also comprise dividing the pose graph into a plurality of pose subgraphs, each pose subgraph comprising one or more respective pose subgraph interior nodes and one or more respective pose subgraph boundary nodes. The operations may also comprise generating one or more boundary subgraphs based on the plurality of pose subgraphs, each of the one or more boundary subgraphs comprising one or more respective boundary subgraph boundary nodes and comprising one or more respective boundary subgraph interior nodes. The operations may also comprise obtaining an optimized pose graph by performing a pose graph optimization. The pose graph optimization may comprise performing a pose subgraph optimization of the plurality of pose subgraphs and performing a boundary subgraph optimization of the plurality of boundary subgraphs.

A feature mapping computer system configured to (i) receive a first localized image including a first photo and a first location; (ii) receive a second localized image including a second photo and a second location; (iii) identify a roadway feature depicted in both the first and second photos; (iv) generate, using a photogrammetry module, a point cloud based upon the first and second photos and first and second locations; (v) generate a localized point cloud by assigning a location to the point cloud based upon at least one of the first and second locations; and (vi) generate an enhanced base map that includes a roadway feature by embedding an indication of the identified roadway feature onto the localized point cloud.

A map updating method and apparatus (800), and a device (900) are disclosed, which can be used in automated driving (Automated driving), intelligent driving (Intelligent Driving), and other fields. The map updating method includes: when an abnormal scenario occurs, obtaining various types of sensing data of the abnormal scenario, calculating a minimum safety boundary based on the sensing data of the abnormal scenario; and updating a map based on the minimum safety boundary obtained through calculation. According to the map updating method, a map updating program can be triggered when the abnormal scenario occurs, without the need to wait for a collection vehicle/crowd-sourcing vehicle. This improves real-time performance of map refreshing, and ensures safety of an automated driving environment.

A method, including providing a device for an individual, storing information regarding a travel schedule or itinerary of the individual, determining a location of the device with a global positioning device, automatically and continuously monitoring the location of the device, comparing the location of the device with the travel schedule or itinerary, detecting a deviation of the device from a travel schedule or itinerary, or detecting an absence of, or a leaving of, the device from an expected location on the travel schedule or itinerary, generating a travel route deviation message and transmitting the travel route deviation message to a first communication device.

A method for building a map includes: acquiring an original grayscale map, preprocessing the original grayscale map to obtain a preprocessed map, binarizing the preprocessed map to obtain a binarized map, performing a boundary filling to the preprocessed map and the binarized map to obtain a boundary-filled preprocessed map and a boundary-filled binarized map, performing a boundary thinning to the boundary-filled binarized map to obtain a thinned binarized map, and performing a boundary thinning to the boundary-filled preprocessed map, according to the thinned binarized map, to obtain a thinned preprocessed map.