Examples disclosed herein involve a computing system configured to (i) receive, from an image-capture device, image-capture metadata that provides information about a set of images that were passively captured by the image-capture device, (ii) based at least on the received image-capture metadata, apply a collection policy for determining whether to collect any of the set of images that have been passively captured by the image-capture device and thereby determine that a selected subset of one or more images in the set of images are to be collected from the image-capture device, (iii) instruct the image-capture device to upload the selected subset of one or more images, and (iv) after instructing the image-capture device to upload the selected subset of one or more images, receive the selected subset of one or more images from the image-capture device.

Systems and methods for updating an electronic map of a facility are disclosed. The electronic map includes a set of map nodes. Each map node has a stored image data associated with a position within the facility. The method includes collecting image data at a current position of a self-driving material-transport vehicle; searching the electronic map for at least one of a map node associated with the current position and one or more neighboring map nodes within a neighbor threshold to the current position; comparing the collected image data with the stored image data of the at least one of the map node and the one or more neighboring map nodes to determine a dissimilarity level. The electronic map may be updated based at least on the collected image data and the dissimilarity level. The image data represents one or more features observable from the current position.

The present invention provides a multi-sensor fusion-based Simultaneous Localization And Mapping (SLAM) mapping method and system for a server, comprising: obtaining a plurality of sensor data regarding a surrounding environment of a moving platform, the plurality of sensor data including point cloud data, image data, inertial measurement unit (IMU) data, and global navigation satellite system (GNSS) data; performing hierarchical processing on the plurality of sensor data to generate a plurality of localization information, wherein one sensor data corresponds to one localization information; obtaining target localization information of the moving platform based on the plurality of localization information; generating a high-precision local map based on the target localization information; and performing a closed-loop detection operation to the high-precision local map to obtain a high-precision global map of the moving platform. The present invention mitigates the technical problem in the related art that easy susceptibility to a surrounding environment leads to low precision.

Methods, systems and computer program products, for map data collection. An anchor point creation mode is activated on a mobile data collection device when the mobile data collection device enters a geographical anchor point creation area. In the anchor point creation mode, the mobile data collection device collects and streams mapping raw data to a remote computing device. A high definition map of the anchor point creation area is created by combining received raw data with mapping data from other data sources. After the map has been created, when a mobile data collection device enters the anchor point creation area, information from the map is used in a data capture mode to refine a determination of a position and/or orientation of the mobile data collection device, and a position and/or orientation of one or more features detected by the mobile data collection device.

A road shape information generator is provided. The road shape information generator includes a sensor value obtainer that obtains a sensor value acting, when a vehicle travels, on the vehicle, a road shape information generator that generates information about a road shape as road shape information based on the sensor value obtained by the sensor value obtainer, and a storage controller that stores, in a road shape information storage, the road shape information generated by the road shape information generator.

The present application discloses a method and an apparatus for generating a high precision map. According to an embodiment, the method comprises: acquiring three-dimensional (3D) laser point cloud data and information related to a grid map for generating the high precision map; determining position information of each piece of the 3D laser point data of the 3D laser point cloud data in the grid map; rendering each pixel point in the grid map, by using the reflection value of corresponding 3D laser point data of the 3D laser point cloud data, in order to generate each of grid images in the grid map; identifying, by using a machine learning algorithm, traffic information of each of the grid images in the grid map; clustering the traffic information of each of the grid images in the grid map to obtain the traffic information of the grid map; and loading the traffic information of the grid map into the grid map to generate the high precision map. The embodiment implements the generating of a high precision map with a high precision and a plurality of dimensions.

A system includes a scouting vehicle. The scouting vehicle includes a spatial location system configured to derive a geographic position of the scouting vehicle. The scouting vehicle further includes a computing device communicatively coupled to the spatial location system and to a communication system, the computing device comprising a processor configured to create a shape on a map based on a drive of the scouting vehicle. The scouting vehicle also includes the communication system configured to transmit the geographic position, a recording of the drive, the shape, or a combination thereof, to a base station.

A method for creating a map by a moving robot includes receiving sensor data regarding a distance to an external object through a distance measurement sensor, and creating a cell-based grid map based on the sensor data. Image processing is performed to distinguish between regions in the grid map and to create a boundary line between the regions. An optimal boundary line is selected from the one or more boundary lines, and a path to the optimal boundary line is planned. The grid map is updated while the moving robot is moving along the path such that the map may be automatically created.

A method for discovering a newly added road, includes: determining trajectory data in a target area within a preset time period, in which the trajectory data includes a plurality of trajectories and attribute information of each trajectory point in respective trajectories; determining trajectory features of a plurality of grids in the target area, according to the plurality of trajectories, the attribute information of each trajectory point in respective trajectories, and position features of the plurality of grids; generating current grid portrait data of the target area, according to the position features and trajectory features of the plurality of grids; and determining newly added road information of the target area, according to the current grid portrait data and historical grid portrait data of the target area.

The present invention generates utilization data of a vehicle, whereby data from a telematics control unit of vehicle is received by a host system, the data comprising at-least location data, speed data, RPM data of the vehicle. The data is classified into plurality of segments, each segment comprising a cluster of location points or GPS points with speed data and RPM data; and each segment is analyzed to identify from the plurality of segments an on-road segments or on-field segments based upon at-least density of location points in the segment; and utilization data of the vehicle for on-road segments and/or on-field segments is generated.