One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

An agricultural work machine includes a geographic position sensor that detects a geographic location of the agricultural work machine. An in-situ sensor detects a value of a dynamic response characteristic of the agricultural work machine corresponding to the geographic location. A predictive model generator generates a predictive model that models a relationship between the terrain feature characteristic and the dynamic response characteristic based on a value of the terrain feature characteristic in a prior information map at the geographic location and a value of the dynamic response characteristic sensed by the in-situ sensor at the geographic location. A predictive map generator generates a functional predictive dynamic response map of the field, that maps predictive values of the dynamic response characteristic to the different geographic locations in the field, based on the values of the terrain feature characteristic in the prior information map and based on the predictive model.

There is provided a digital map product comprising data informative of a location of a zone in a surface area including a subsurface utility infrastructure (SUI), the digital map product being derivative of a method comprising: receiving a digital image of the surface area; identifying a plurality of surface features; for each of the plurality of surface features: calculating an indication of utility location (IUL) from the respective surface feature and location, wherein the IUL is one of: a location of a point of the SUI, a location of a zone of the SUI, a location of a zone from which the SUI is absent, thereby giving rise of a plurality of IULs; and defining a location of a zone including the SUI in accordance with, at least, the plurality of IULs wherein at least one surface feature of the plurality of surface features is a public works surface marking.

A method, apparatus and computer program product for activating a flood event warning are described herein. In the context of a method, a location may be identified as a flood prone location. Data relating to the flood prone location may be received from one or more remote devices. The method may determine a flood confidence for the flood prone location based upon the data. The method may identify an active flood event for the flood prone location based on the flood confidence and cause a flood event warning to be activated in an instance in which the active flood event is identified.

The present disclosure relates generally to identification of drivable surfaces in connection with autonomously performing various tasks at industrial work sites and, more particularly, to techniques for distinguishing drivable surfaces from non-drivable surfaces based on sensor data. A framework for the identification of drivable surfaces is provided for an autonomous machine to facilitate it to autonomously detect the presence of a drivable surface and to estimate, based on sensor data, attributes of the drivable surface such as road condition, road curvature, degree of inclination or declination, and the like. In certain embodiments, at least one camera image is processed to extract a set features from which surfaces and objects in a physical environment are identified, and to generate additional images for further processing. The additional images are combined with a 3D representation, derived from LIDAR or radar data, to generate an output representation indicating a drivable surface.

A method for detecting an operating terrain is provided. The method includes obtaining point cloud data of an operating region that are collected by a laser radar at a current time, including three-dimensional coordinates of a plurality of sampling points. The operating region is divided into a plurality of grids, each having a corresponding height value. The method includes for any grid determining an input point of the grid from the plurality of sampling points, based on the three-dimensional coordinates. The method includes determining a type of the input point, based on a height coordinate of the input point and the height value of the grid. The type includes a noise point and a ground point. The method includes in response to determining that the input point is the ground point, updating the height value of the grid based on the height coordinate of the input point.

This invention relates to a system of lawn mowing and caring services. It comprises a lawn profile data information management system having lawn profile data stored therein; at least one mobile device for registered users, which communicates wirelessly with the lawn profile data information management system for requesting services; and at least one robotic lawn mower or lawn robot which works with the mobile device to acquire requisite lawn profile data from the lawn profile data information management system for the services before the robotic lawn mower or lawn robot can perform the requisite mowing or caring services on the specific piece of lawn.

An imaging system can include a first and second camera configured to capture first and second sets of oblique images along first and second scan paths, respectively, on an object area. A drive is coupled to a scanning mirror structure, having at least one mirror surface, and configured to rotate the structure about a scan axis based on a scan angle. The first and second cameras each have an optical axis set at an oblique angle to the scan axis and include a respective lens to focus first and second imaging beams reflected from the mirror surface to an image sensor located in each of the cameras. The first and second imaging beams captured by their respective cameras can vary according to the scan angle. Each of the image sensors captures respective sets of oblique images by sampling the imaging beams at first and second values of the scan angle.

A computer-implemented method for controlling an excavation task by an autonomous excavation vehicle comprising a scanning device, the excavation task being described by a target map, the method comprising using an excavation vehicle control system for: a) according to data from the scanning device, maintaining a map representing current terrain; b) moving a sensor-equipped digging implement for executing an excavation operation; c) receiving data indicative of current terrain topography from the sensor; d) updating the maintained map according to the data indicative of current terrain topography; and e) calculating an excavation operation according to the difference between the maintained map and the target map.

A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include receiving a navigation route for a mobile robot. The navigation route includes a sequence of waypoints connected by edges. Each edge corresponds to movement instructions that navigate the mobile robot between waypoints of the sequence of waypoints. While the mobile robot is traveling along the navigation route, the operations include determining that the mobile robot is unable to execute a respective movement instruction for a respective edge of the navigation route due to an obstacle obstructing the respective edge, generating an alternative path to navigate the mobile robot to an untraveled waypoint in the sequence of waypoints, and resuming travel by the mobile robot along the navigation route. The alternative path avoids the obstacle.