A computer implemented method for guiding traffic participants, especially pedestrians, especially visually impaired and blind people, especially for guiding in urban environments, between at least two places wherein the method contains the following steps: A. Providing a multi-modal three-dimensional map; B. Calculating a route based on the multi-modal three-dimensional map connecting the at least two places over at least one intermediate waypoint; C. Determining precise location of the traffic participant, preferably by using the multi-modal three-dimensional map; and D. Setting beacons along the path at the waypoints.

In various examples, operations include obtaining, from a machine learning model, feature classifications that correspond to features of objects depicted in images of a geographical area in which the images are provided to the machine learning model. The operations may also include annotating the images with three-dimensional representations that are based on the obtained feature classifications. Further, the operations may include generating map data corresponding to the geographical area based on the annotated images.

Disclosed is a system and a method for geo-referencing an object on a floor. The method comprises receiving a camera frame providing view of a section of the floor, receiving a detailed floor plan of the floor, selecting a first area from the section of the floor in which the object needs to be tracked and defining a plurality of reference points. Herein, at least one of the pluralities of reference points is close to a mid-point along one of corresponding axes of the first area. The method further comprises mapping the plurality of reference points to the floor plan, processing the camera frame to determine if the object has been placed in the first area and geo-referencing the one or more object in the floor plan if the object is determined to be placed in the first area.

A method of controlling flight of an unmanned aerial vehicle (UAV) includes collecting, while the UAV traverses a flight path, a set of images corresponding to different fields of view of an environment around the UAV using multiple image capture devices. Each of the multiple image capture devices includes one of the different fields of view. The method further includes extracting a set of feature points from the set of images and selecting a subset of feature points from the set of feature points to be stored based on a number of image capture devices that collect images including the subset of feature points to aid navigation of the UAV.

An apparatus for generating a map includes a processor configured to detect a stop line of an intersection from a bird's-eye view image, detect from the bird's-eye view image an intersection area including an intersection and roads connected to the intersection, detect at least either one of an entry lane for entering the intersection and an exit lane for exiting the intersection in a road from which the stop line is extracted, of the roads, based on the ratio of the length of the stop line to the width of the road, and generate a lane network representing a connection relationship between lanes in the intersection so as to connect, for each of the roads, the entry lane of the road to the exit lane of another of the roads to which a vehicle is allowed to proceed from the entry lane of the road.

A computer implemented method for guiding traffic participants, especially pedestrians, especially visually impaired and blind people, especially for guiding in urban environments, between at least two places wherein the method contains the following steps: A. Providing a multi-modal three-dimensional map; B. Calculating a route based on the multi-modal three-dimensional map connecting the at least two places over at least one intermediate waypoint; C. Determining precise location of the traffic participant, preferably by using the multi-modal three-dimensional map; and D. Setting beacons along the path at the waypoints.

An apparatus for generating a map includes a processor configured to detect a stop line of an intersection from a bird's-eye view image, detect from the bird's-eye view image an intersection area including an intersection and roads connected to the intersection, detect at least either one of an entry lane for entering the intersection and an exit lane for exiting the intersection in a road from which the stop line is extracted, of the roads, based on the ratio of the length of the stop line to the width of the road, and generate a lane network representing a connection relationship between lanes in the intersection so as to connect, for each of the roads, the entry lane of the road to the exit lane of another of the roads to which a vehicle is allowed to proceed from the entry lane of the road.

The present application discloses a method and apparatus for identifying an updated road, a device and a computer storage medium, and relates to the field of big data technologies. A specific implementation solution is as follows: comparing a road area extracted based on the latest satellite image with a road area extracted based on a historical satellite image, to obtain a candidate updated road; mapping the candidate updated road into road network data according to a coordinate position of the candidate updated road; acquiring a user trajectory set corresponding to the candidate updated road within a recent preset period; and identifying, based on a matching result between the user trajectory set and the road network data, whether the candidate updated road is an actual updated road. Updated roads can be more accurately identified through the method according to the present application.

An autonomous vehicle navigates using a digital map stored in memory. In one approach, the vehicle plans a navigation route that includes a geographic location (e.g., a location on a road to be traveled by the vehicle). An unmanned aerial vehicle (UAV) collects sensor data at the geographic location (e.g., in advance of travel on the road). The collected sensor data is processed to generate map data for objects or other features at the geographic location. The digital map is updated using the generated map data.

A refuse vehicle system includes a refuse vehicle, a drone, and a controller. The drone includes a GPS and a sensor configured to provide data relating to a status of refuse within a scan area. The controller is configured to receive data from the sensor of the drone, determine the status of refuse within the scan area based on the data from the sensor of the drone, and at least one of generate a route for the refuse vehicle based on the status of refuse within the scan area, modify a route for the refuse vehicle based on the status of refuse within the scan area, and provide a signal to the refuse vehicle regarding the status of refuse within the scan area.