A position determination system for determining a position of a device movable in an environment comprises a position determination unit and a memory unit. An environmental map of the environment is stored in the memory unit. The position determination unit is configured to receive measured distance values of an environmental sensor of the movable device and to determine the position of the movable device by a comparison of the measured distance values with the environmental map. The stored environmental map comprises at least one block, with the block comprising an arrangement of map values representing the environment and with the map values representing distance values from a margin of the environment. The block of the environmental map is stored in the memory unit as a coefficient of a linear combination of basis functions, with the linear combination of the basis functions approximating the map values of the block.

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.

A server system is provided that includes a platform server system configured to store platform map data that is geospatially referenced and includes building outline data for one or more buildings, and provide the platform map data to client computer devices. The server system further includes a tenant bounded server system including one or more processors configured to store building floorplan data for a tenant entity, and provide building floorplan data to client computer devices that are authenticated for the tenant entity. The one or more processors of the tenant bounded server system are further configured to receive a set of building floorplan data for a target building included in the platform map data, determine geospatial reference data for the set of building floorplan data by aligning the set of building floorplan data with building outline data of the target building.

An example system includes a sensor for obtaining information about an object in an environment and one or more processing devices configured to use the information in generating or updating a map of the environment. The map includes the object and boundaries or landmarks in the environment. The map includes a static score associated with the object. The static score represents a likelihood that the object will remain immobile within the environment. The likelihood may be between certain immobility and certain mobility.

A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.

In one implementation, a method of generating an enhanced reality (ER) map is performed by a device including one or more processors and non-transitory memory. The method includes selecting ER setting representations based on clusters of images and displaying an ER map including the ER setting representations along a path.

A robot may include a LiDAR sensor, and a processor configured to acquire, based on a sensing value of the LiDAR sensor, a first map that covers a space where the robot is located, detect one or more obstacles existing in the space based on the sensing value of the LiDAR sensor, acquire a number of times that each of a plurality of areas in the first map is occupied by the one or more obstacles, based on location information of the one or more obstacles, determine an obstacle area based on the number of times that each of the plurality of areas is occupied by the one or more obstacles, and acquire a second map indicating the obstacle area on the first map to determine a driving route of the robot based on the second map.

An embodiment of the invention may include a method, computer program product and system for digital indoor navigation. An embodiment may include creating navigational directions corresponding to a navigation path on a digital indoor mapping image. The navigational directions comprise prompts for user action and contextual information of at least one determined area type for at least one distinct area depicted within the digital indoor mapping image through which the navigation path traverses. An area type determination for a distinct area is based on an identified color used as a fill for the distinct area. An embodiment may include presenting the created navigational directions along with the navigation path.

A method for finding door location in an automated way based on observations of people that are equipped with a device whose position is determined acoustically. By observing positioning transitions across internal structures such as walls, the location of doors can be automatically identified.

The present invention provides a method and a device for map editing, includes: controlling a robot to build a node at a predefined time interval and to save a submap produced by a scanning process performed by the robot on a workspace at each node, when the robot moves in the workspace; combining the submaps to obtain an entire map through a first algorithm, where plural node marks respectively representing the submaps are shown on the entire map; selecting two node marks having parts determined to be abnormal on the entire map, and showing the submaps represented by the two node marks; and overlapping parts having same structure features in the two submaps correspondingly, and combining the parts through a second algorithm, and applying the combined parts into the entire map again to form a corrected entire map.