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.

Autonomously driven vehicles operate in rain, snow and other adverse weather conditions. An on-board vehicle sensor has a beam with a diameter that is only intermittently blocked by rain, snow, dust or other obscurant particles. This allows an obstacle detection processor is to tell the difference between obstacles, terrain variations and obscurant particles, thereby enabling the vehicle driving control unit to disregard the presence of obscurant particles along the route taken by the vehicle. The sensor may form part of a LADAR or RADAR system or a video camera. The obstacle detection processor may receive time-spaced frames divided into cells or pixels, whereby groups of connected cells or pixels and/or cells or pixels that persist over longer periods of time are interpreted to be obstacles or terrain variations. The system may further including an input for receiving weather-specific configuration parameters to adjust the operation of the obstacle detection processor.

An autonomous driving device includes a map recording a content having different type for each position while one or a plurality of contents and positions are associated with each other, an acquisition unit acquiring the content corresponding to a first position on the map, a specification storage unit storing a plurality of autonomous driving modes of the vehicle and the type of content necessary for the execution of the modes in association with each other, a selection unit selecting an executable autonomous driving mode based on the type of content acquired by the acquisition unit and the type of content stored in the specification storage unit, and a control unit controlling the vehicle at the first position in the selected autonomous driving mode, the selection unit determines one autonomous driving mode based on an order of priority set in advance when there is a plurality of executable autonomous driving modes.

An example method includes gathering, via a first module of a first type, first simultaneous localization and mapping data and gathering, via a second module of a second type, second simultaneous localization and mapping data. The method includes generating, via a simultaneous localization and mapping module, a first map based on the first simultaneous localization and mapping data and the second simultaneous localization and mapping data, the first map being of a first map type and generating, via the simultaneous localization and mapping module, a second map based on the first simultaneous localization and mapping data and the second simultaneous localization and mapping data, the second map being of a second map type. The map of the first type is used by vehicles with module(s) of the first and/or second types and the map of the second type is used by vehicles with a module of the second type exclusively.

An agricultural machine includes a vehicle body, a travel switch operable to issue a command to start autonomous travel of the vehicle body, and an autonomous travel controller to perform autonomous travel of the vehicle body based on a planned travel line when the command is issued. When the command is issued by operating the travel switch, if at least one of a positional deviation between the planned travel line that is selected and the vehicle body and an orientational deviation between the planned travel line and an orientation of the vehicle body is greater than or equal to a corresponding one of respective first thresholds, the autonomous travel controller is configured or programmed to perform line alignment to make the positional deviation and the orientational deviation less than the respective first thresholds.

An agricultural machine includes a vehicle body, an obstacle detector to detect obstacles, an autonomous travel controller to perform autonomous travel of the vehicle body, the autonomous travel controller being configured or programmed to, when performing the autonomous travel, stop the vehicle body based on detection information about an obstacle detected by the obstacle detector, and a mode switch to switch a mode during the autonomous travel in an agricultural field between an effective mode in which the stopping of the vehicle body based on the detection information is allowed and an ineffective mode in which the stopping of the vehicle body based on the detection information is not allowed.

A method includes identifying, using at least one processor, a first point associated with an uncertain location of an object in a space and a polynomial curve associated with an uncertain location of a feature in the space. The method also includes determining, using the at least one processor, a probabilistic proximity of the object and the feature. The probabilistic proximity is determined by identifying a second point on the polynomial curve, transforming an uncertainty associated with the polynomial curve into an uncertainty associated with the second point, and identifying the probabilistic proximity of the object and the feature using the first and second points and the uncertainty associated with the second point.

A system and method for providing online multi-LiDAR dynamic occupancy mapping that include receiving LiDAR data from each of a plurality of LiDAR sensors. The system and method also include processing a region of interest grid to compute a static occupancy map of a surrounding environment of the ego vehicle and processing a dynamic occupancy map. The system and method further include controlling the ego vehicle to be operated based on the dynamic occupancy map.

This disclosure is directed to calibrating sensors mounted on an autonomous vehicle. A dense depth map can be generated in a two-dimensional camera space using point cloud data generated by one of the sensors. Image data from another of the sensors can be compared to the dense depth map in the two-dimensional camera space. Differences determined by the comparison can indicate alignment errors between the sensors. Calibration data associated with the errors can be determined and used to calibrate the sensors without the need for calibration infrastructure.

An autonomous cleaning device, a method of control the autonomous cleaning device, and a storage medium are provided. The device includes a body, and a cleaning assembly, a driving assembly, wheels, a plurality of detection sensors and a controller on the body. The plurality of detection sensors are configured to transmit detection signals and receive reflection data of the detection signals reflected by an obstacle. The controller is configured to obtain reflection data reflected by the obstacle and received by the plurality of detection sensors, determine whether the obstacle has a gap according to the reflection data received by the detection sensors, and in response to the obstacle having the gap, control the autonomous cleaning device to travel, by sending control instructions to driving assembly, according to the reflection data at different time points and basic information of the autonomous cleaning device.