A map update control method and a map update control system for a vision robot are disclosed. The map update control method includes the following steps: S1, measured data of the map attributes is acquired; S2, when it is detected that the vision robot completes a traversal of the preset working region, whether the current map and a prestored historical map meet a preset matching degree is judged according to the map attribute measured data, in a case that the current map and the prestored historical map meet the preset matching degree, it is determined to store the current map; and S3, when it is determined to store information of the map attributes corresponding to the current map, the information of the map attributes corresponding to the current map is written in a map storage medium, to update information of the map attributes corresponding to the historical map.

An information map is obtained by an agricultural system. The information map maps values of a topographic characteristic to different geographic locations in a field. An in-situ sensor detects values of an environmental characteristic as an agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts the environmental characteristic at different locations in the field based on a relationship between the values of the topographic characteristic and the values of the environmental characteristic detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

A navigation map for an at least partially automated mobile platform (130) is proposed, comprising: Descriptions of courses of a plurality of lanes (120), each lane (120) of the plurality of lanes having a plurality of lane segments (150); and Off-road environment data (140) of the plurality of lanes (120), wherein the off-road environment data is assigned to respective lane segments (150) of the respective lane (120), and wherein the off-road environment data includes an emergency drivability rating for an off-road environment (140) of the respective lane segment (150).

The present disclosure is directed to collecting and processing data from computing devices of a plurality of autonomous vehicles (AVs). The data received from each of these AV computing devices may include raw sensor data or data that has been generated using data received by one or more sensors at respective AVs. Once this data is collected and associated with discrete locations and times, the data may be evaluated and used to generate mappings of various sorts. These mappings may include mappings of underground features generated based on an evaluations of vibration data. Alternatively, or additionally, these mapping may include mappings of landscape features, atmospheric features, or the locations of aircraft from data associated with certain types of sensing apparatus, for example radar apparatus or light detecting and ranging (LiDAR) apparatus.

A method for searching a path by using a 3D reconstructed map includes: receiving 3D point-cloud map information and 3D material map information; clustering the 3D point-cloud map information with a clustering algorithm to obtain clustering information, and identifying material attributes of objects in the 3D point-cloud map information with a material neural network model to obtain material attribute information; fusing the those map information based on their coordinate information, thereby outputting fused map information; identifying obstacle areas and non-obstacle areas in the fused map information based on an obstacle neural network model, the clustering information, and the material attribute information; and generating 3D path information according to the non-obstacle areas. Since the 3D path information is generated based on those map information, the obstacle areas and flight spaces are effectively determined to generate an accurate flight path.

An apparatus and method are presented comprising one or more sensors or cameras configured to rotate about a central motor. In some examples, the motor is configured to travel at a constant linear speed while the one or more cameras face downward and collect a set of images in a predetermined region of interest. The apparatus and method are configured for image acquisition with non-sequential image overlap. The apparatus and method are configured to eliminate gaps in image detection for fault-proof collection of imagery for an underwater survey. In some examples, long baseline (LBL) is utilized for mapping detected images to a location. In some examples, ultra-short baseline (USBL) is utilized for mapping detected images to a location. The apparatus and method are configured to utilize a simultaneous localization and mapping (SLAM) approach.

This specification describes systems and methods for generating a mapping of a physical space from point cloud data for the physical space. The methods can include receiving the point cloud data for the physical space, filtering the point cloud data to, at least, remove sparse points from the point cloud data, aligning the point cloud data along x, y, and z dimensions that correspond to an orientation of the physical space, and classifying the points in the point cloud data as corresponding to one or more types of physical surfaces. The methods can also include identifying specific physical structures in the physical space based, at least in part, on classifications for the points in the point cloud data, and generating the mapping of the physical space to identify the specific physical structures and corresponding contours for the specific physical structures within the orientation of the physical space.

Methods and systems employ at least two motion estimators to form respective estimates of position of a mobile device over time. The estimates of position over time are based on sensor data generated at the mobile device. Each motion estimator is associated with a respective reference frame, and each respective estimate of position includes one or more estimate components. A transformation from the reference frame associated with a second motion estimator to the reference frame associated with a first motion estimator is determined. The transformation is determined based at least in part on at least one estimate component of the one or more estimate components of the estimates of position formed by each of the first and second motion estimators.

One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a material dynamics characteristic value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that predicts a predictive material dynamics characteristic value at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the material dynamics characteristic value detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

An apparatus for artificial intelligence (AI) bathymetry is disclosed. The apparatus includes a sonic unit attached to a boat, the sonic unit configured to generate a plurality of metric data as a function of a plurality of ultrasonic pulses and a plurality of return pulses. An image processing module is configured to generate a bathymetric image as a function of the plurality of metric data, identify, as a function of the bathymetric image, an underwater landmark, and register the bathymetric image to a map location as a function of the underwater landmark. A communication module is configured to transmit the registered bathymetric image to at least a computing device. An autonomous navigation module is configured to determine a heading for the boat as a function of a path datum and command boat control to navigate the boat as a function of the heading.