An autonomous vehicle according to an embodiment of the present disclosure includes: a driver to control driving of the autonomous vehicle; a sensor to obtain traveling information; and a processor to: identify a current location of the autonomous vehicle, based on the traveling information and a 3-dimensional first point cloud map of a target area, determine a 2-dimensional global path, which is from the current location to a destination location of the autonomous vehicle, based on a 2.5-dimensional first occupancy grid map, which indicates a global traversability, generate a 2.5-dimensional second occupancy grid map, which indicates a local traversability that is determined based on a second point cloud map obtained in real-time according to the traveling information, and control the driver by applying a 2-dimensional local path from the current location to the destination location, to the 2-dimensional global path, based on the second occupancy grid map.

A method of filtering a data set generated by a sensor on a material handling vehicle includes receiving a series of data points from the sensor on the material handling vehicle, mapping each data point in the series of data points to a cell of a voxel grid, determining a number of data points within each cell of the voxel grid, and comparing the number of data points within each cell of the voxel grid to a threshold value to reduce the number of data points. When the number of data points within a cell of the voxel grid is below the threshold value, the cell of the voxel grid is marked as unoccupied. When the number of data points within a cell of the voxel grid is equal to or above the threshold value, the cell of the voxel grid is marked as occupied.

Path planning and navigation for a mobile machine is disclosed. A path planning method plans a path for the mobile machine having a plurality of sensors by: receiving, from each of the sensors of the mobile machine, sensor data; creating, based on the received sensor data from each of the sensors, a plurality of local sensor layers each corresponding to the received sensor data from each of the sensors; creating a local map by integrating all the created local sensor layers; inflating the local map; creating a global costmap by fusing an inflated global map and the inflated local map; planning, according to the costmap, the path for navigating the mobile machine; and providing the planned path to the mobile machine for navigating the mobile machine using the planned path.

A mobile robot system is described in having a mobile robot and cloud system. The mobile robot leverages cloud computing to offload Neural Radiance Fields (NeRF) based 3D scene reconstruction. The mobile robot advantageously adopts techniques for view filtering and next-best view selection that optimize the image collection process necessary for training an NeRF model with the cloud system. These techniques enable the mobile robot to discard redundant images that do not provide significant new information about the environment. Additionally, these techniques enable the mobile robot to strategically select next-best views that maximize the information gain, while minimizing a total number of images required and the time required to capture the images. These techniques provide a significant reduction in the overall bandwidth required for providing image data to the cloud system and can result in a more accurate and higher quality 3D reconstruction of the environment.

The technology disclosed includes systems and methods for preparing a segmented occupancy grid map based upon image information of an environment in which a robot moves. The image information is captured by at least one visual spectrum-capable camera and at least one depth measuring camera. The system includes logic to receive image information captured by at least one visual spectrum-capable camera and location information captured by at least one depth measuring camera located on a mobile platform. The system includes logic to extract from the image information, features in the environment. The system includes logic to determine a 3D point cloud of points having 3D information. The system includes logic to determine, from the 3D point cloud, an occupancy map of the environment. The system includes logic to segment the occupancy map into a segmented occupancy map of regions that represent rooms and corridors in the environment.

A system for creating a representation of visible space around a machine using a previously determined combined occupancy grid. The system includes a plurality of sensors and an electronic processor. The electronic processor is configured to determine a combined occupancy grid associated with a first time, wherein the combined occupancy grid is determined based on sensor data associated with the first time and received from the plurality of sensors. The electronic processor is also configured to receive, from the plurality of sensors, sensor data associated with a second time and determine a representation of visible space associated with the second time based on the combined occupancy grid associated with the first time and the sensor data associated with the second time. The electronic processor is further configured to control a movement of the machine based on the representation of visible space associated with the second time.

A method for planning a path for a mobile object is disclosed. The method includes: generating a plurality of costmap layers based at least on locations of the obstacles in a navigation area, current locations and/or current planned paths of other mobile objects in the navigation area, and historical locations of other mobile objects in the navigation area; generating a master costmap based on the plurality of costmap layers; and planning a path to a target location based on the master costmap. An apparatus for planning a path for a mobile object is also disclosed. A controller for a mobile object, a mobile object and a computer-readable storage medium are also disclosed.

A machine guidance system and method utilize optical sensors, position sensors, and movement sensors to generate point cloud data for a construction asset. Data encompassing terrain features, obstacles, and the real-world position and orientation of the asset and its attachment are calculated. The fused data is used to autonomously control the movement of the asset and the position of the attachment to maintain a cutting edge at a target grade. As a result, terrain mapping and obstacle detection are performed in real-time, enabling safe and efficient operation without predefined paths. In this manner, applications include automated earthmoving, grading, and material handling in dynamic construction environments.

An apparatus for localizing a robot having robustness to a dynamic environment includes a map building unit which builds a map based on SLAM; a localizing unit which acquires first feature from sensor data acquired by a sensor mounted in a robot and localizes the robot using the first feature acquired from the sensor data based on the map built by the map building unit; and a map updating unit which reduces an error caused by the movement of the robot by correcting the first feature using an estimated position of the robot with regard to a feature obtained from a static object, among the first features acquired by the localizing unit.

A robot control apparatus and a method thereof are provided. The robot control apparatus includes at least one sensor and a processor. The processor may be configured to obtain, via the at least one sensor and from an area within a designated distance from a robot, data points associated with one or more target objects around the robot, determine a first group of data points and a second group of data points, determine, based on an angle difference between the first group and the second group and based on a height difference between the first group and the second group, whether the first segmented area corresponds to a ground, and control, based on the determination of whether the first segmented area corresponds to the ground, movement of the robot.