A virtual teaching system includes a semi-autonomous device to perform a task such as cleaning a target environment. The semi-autonomous device includes one or more sensors configured to record environmental data from the target environment that can be used to construct a virtual environment. The semi-autonomous device is operably coupled to an analysis system. The analysis system includes a processor to perform multiple functions, such as constructing the virtual environment from the recorded environmental data and supporting operation of a user interface. The user interface can be operably coupled to the processor, allowing a human operator to teach a virtual device in the virtual environment to perform an action sequence. Once the virtual device has been taught an action sequence in the virtual environment, the analysis system can transfer the recorded action sequence to the semi-autonomous device for use in the target environment.

Methods described herein relate to updating a map model. Methods may include receiving data from a vehicle pertaining to a location; comparing the received data pertaining to the location to historical data pertaining to the same location; and based at least in part upon a comparison of the received data pertaining to the location and the historical data pertaining to the same location, generating an internal model for the location. A corresponding apparatus and computer program product are also provided.

An embodiment user equipment includes an image sensor configured to photograph a surrounding image, a display module, a communicator configured to communicate with a server, a database configured to store visual positioning system (VPS) map information based on augmented reality (AR) and two dimensional (2D) map information, and a controller configured to generate 2D route information based on the 2D map information and delivery information received from the server, generate three dimensional (3D) route information based on the delivery information and the VPS map information, and control the display module to display the 2D route information or the 3D route information based on a distance between the user equipment and a point of interest (POI) included in the delivery information, the 3D route information being based on augmented reality.

Disclosed are methods of navigating a robot in a crowd involving mapping a local crowd and applying a clustering algorithm that reconstructs a crowd-flow map capturing movement patterns of pedestrians in the local crowd; using a flow-matching metric, localizing the robot in the crowd-flow map; and following a movement pattern in the crowd-flow map using a hierarchical crowd-driven planning on a long time horizon and a short time horizon.

A system for acquiring data from vehicle in real time for mapping infrastructure using blockchain technology.

An ADV may determine whether there is preexisting map data for an environment or geographical area/location where the ADV is located/travelling. If there is no preexisting data, the ADV may generate map data based on sensor data obtained from one or more sensors of the ADV. The ADV may determine a path for the ADV based on the generated map data. If there is preexisting map data, the ADV may determine a path for the ADV based on the preexisting map data.

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.

Embodiments of the present disclosure provide a map updating method and apparatus, a device, a server, and a storage medium, which relate to the field of artificial intelligence, and in particular, to the field of autonomous parking. The specific implementation solution is: an intelligent vehicle obtains driving data collected by a vehicle sensor of its own vehicle on a target road section, where the driving data at least includes first video data related to an environment of the target road section, and determines at least one image feature corresponding to each first image frame in the first video data, where the image feature at least includes an image local feature related to the environment of the target road section, and then updates map data corresponding to the target road section according to the at least one image feature corresponding to each first image frame in the first video data.

An approach is provided for estimating lane pavement conditions based on street parking events. For example, the approach involves map-matching a vehicle park-in event, a vehicle park-out event, or a combination thereof to a lane of a road segment. The approach also involves calculating an adjusted pavement condition of the lane based on the map matched park-in event, the map-matched park-out event, or a combination thereof, wherein the adjusted pavement condition accounts for a reduction of a weather effect on a pavement condition of the lane caused by one or more vehicles parking in the lane. The approach further involves providing the adjusted pavement condition of the lane as an output.

The present disclosure relates to a control device, a control method, and a mobile object that effectively use storage capacity while saving the storage capacity and efficiently plan a movement route by storing or forgetting (deleting) a past detailed map according to importance during autonomous movement. When a global route is set, by storing or forgetting (deleting) a detailed map according to the importance on the global route to plan a local route from a frequently used global route, it is possible to efficiently plan a local route by combining detailed maps stored in the past.