A method may include obtaining sensor data about a total measurable world around an autonomous vehicle. The sensor data may be captured by sensor units co-located with the autonomous vehicle. The method may include generating a mapping dataset including the obtained sensor data and identifying data elements that each represents a point in the mapping dataset. The method may include sorting the data elements according to a structural data categorization that is a template for a high-definition map of the total measurable world and determining a mapping trajectory of the autonomous vehicle. The mapping trajectory may describe a localization and a path of motion of the autonomous vehicle. The method may include generating the high-definition map based on the structural data categorization and relative to the mapping trajectory of the autonomous vehicle, and the high-definition map may be updated based on the path of motion of the autonomous vehicle.

A worker constructs a map necessary for inspection of infrastructure equipment using data acquired using a target imaging device as data necessary for map generation, thereby reducing the cost necessary for transportation and the like of an autonomous inspection apparatus, and moreover, travel evaluation and inspection evaluation are performed using an evaluation function, and a map necessary for the inspection can be corrected based on a travel route of an autonomous travel route and an inspection result based on an evaluation result, whereby the introduction cost can be reduced.

An agricultural harvesting machine includes crop processing functionality configured to engage crop in a field, perform a crop processing operation on the crop, and move the processed crop to a harvested crop repository, and a control system configured to identify a weed seed area indicating presence of weed seeds, and generate a control signal associated with a pre-emergence weed seed treatment operation based on the identified weed seed area.

A method of controlling a mobile robot includes a first basic learning process of generating a first basic map based on environment information acquired in a traveling process, a second basic learning process of generating a second basic map based on environment information acquired in a separate traveling process, and a merging process of merging the first basic map and the second basic map to generate a merged map.

An example control system includes a memory and at least one processor to obtain image data from a given region and perform image analysis on the image data to detect a set of objects in the given region. For each object of the set, the example control system may classify each object as being one of multiple predefined classifications of object permanency, including (i) a fixed classification, (ii) a static and fixed classification, and/or (iii) a dynamic classification. The control system may generate at least a first layer of a occupancy map for the given region that depicts each detected object that is of the static and fixed classification and excluding each detected object that is either of the static and unfixed classification or of the dynamic classification.

An infrastructure is provided for improving the safety of autonomous systems. An autonomous vehicle management system (AVMS) controls one or more autonomous functions or operations performed by a vehicle or machine such that the autonomous operations are performed in a safe manner. The AVMS is capable of dynamically controlling the behavior of sensors associated with a vehicle. For example, for a sensor, the AVMS can dynamically change and control what sensor data is captured by the sensor and/or communicated from the sensor to the AVMS (e.g., granularity/resolution, field of view, control zoom), when the data is captured by the sensor and/or communicated by the sensor to the AVMS (e.g., on-demand, according to a schedule), and how the data is captured by the sensor and/or communicated from the sensor to the AVMS (e.g., communication format, communication protocol, rate of data communication).

A system and method for measuring three-dimensional (3D) coordinate values of an environment is provided. The system includes a movable base unit a first scanner and a second scanner. One or more processors performing a method that includes causing the first scanner to determine first plurality of coordinate values in a first frame of reference based at least in part on a measurement by at least one sensor. The second scanner determines a second plurality of 3D coordinate values in a second frame of reference as the base unit is moved from a first position to a second position. The determining of the first coordinate values and the second plurality of 3D coordinate values being performed simultaneously. The second plurality of 3D coordinate values are registered in a common frame of reference based on the first plurality of coordinate values.

The present disclosure relates generally to techniques for the kinematic estimation and dynamic behavior estimation of autonomous heavy equipment or vehicles to improve navigation, digging and material carrying tasks at various industrial work sites. Particularly, aspects of the present disclosure are directed to obtaining a set of sensor data providing a representation of operation of an autonomous vehicle in a worksite environment, estimating, by a trained model comprising a Gaussian process, a set of output data based on the set of sensor data, controlling an operation of the autonomous vehicle in the worksite environment using input data derived from the set of sensor data and the set of output data, obtaining actual output data from the operation of the autonomous vehicle in the worksite environment, and updating the trained model with the input data and the actual output data.

The method of preparing a navigation autonomy map for a vehicle covering a zone including path segments, comprising the following steps: identifying the path segments for which the map needs to be prepared; for each path segment, calculating a primary autonomy index in accordance with at least two distinct functions; and calculating for each path segment a final autonomy index by taking a weighted average of the primary autonomy indices. An autonomy map, an application of the autonomy map, and a vehicle using such an autonomy map.

A mobile object configured for movement in an area equipped with VLC illumination sources comprises a light sensor arranged to detect illumination from at least one of the illumination sources within the view of the light sensor; a computer arranged to determine from the detected illumination (i) a position of the mobile object relative to the at least one illumination source and (ii) the identifier of the at least one illumination source; and a transceiver. The transceiver can receive from another mobile object a message comprising the position of the other mobile object relative to a source of illumination, and the identifier of that source of illumination. From this, the computer determines from its position and the message a distance from the other mobile object. A mobile object which transmits such a message is also envisaged.