Abstract: The disclosure relates to a method for creating an object map for a factory environment by using sensors present in the factory environment, wherein at least one part of an object in the factory environment has information relating to its position recorded by at least two of the sensors, wherein the information recorded by the sensors is transmitted to a server associated with the sensors, and wherein the server is used to take the information recorded and transmitted by the sensors as a basis for creating an object map for the factory environment having a position of the at least one part of the object.

Abstract: A moving robot includes: a sensor to acquire terrain information; a memory to store node data for at least one node; and a controller, and the controller determines whether at least one open movement direction exists among a plurality of movement directions, based on sensing data and the node data, generates a new node in the node data when at least one open movement direction exists, determines any one of the open movement directions as a traveling direction for the robot, determines whether at least one of the nodes needs to be updated exists, based on the node data when the open movement direction does not exist, controls the moving robot to move to one of the nodes that need to be updated, and generates of a map including the at least one node, based on the node data, when the node that needs to be updated does not exist.

Abstract: A method for performing recuperation for an ego vehicle, which has at least one electrical machine and is located with at least one further road user on a route in a traffic situation, wherein a drone which has at least one camera is used, wherein the ego vehicle is accompanied on the route by the flying drone, wherein the traffic situation in which the ego vehicle is located is acquired using the at least one camera, wherein the recuperation for the ego vehicle is performed in dependence on the acquired traffic situation, wherein mechanical energy of the ego vehicle is converted into electrical energy using the at least one electrical machine during performance of the recuperation for the ego vehicle.

Abstract: A vehicle location and control system determines a signal-derived separation distance between antennas disposed onboard a vehicle based on signals received by the antennas from an off-board source. The system determines an input-derived separation distance between the antennas based on input offset distances of the antennas from a designated location on the vehicle, determines a difference between the input-derived separation distance and the signal-derived separation distance, and activates or deactivates an automated route identification system that determines which route of several different routes that the vehicle is disposed upon based on the difference that is determined.

Abstract: An unmanned following vehicle includes a controller that controls the unmanned following vehicle to move in parallel with a moving object by following the moving object on a left side or a right side of the moving object. For example, the controller adjusts a moving speed of the unmanned following vehicle according to a Y-axis coordinate difference value, which is a difference value between a position of the moving object and a position of the unmanned following vehicle in a forward direction. The controller also adjusts a steering direction and a steering angle of the unmanned following vehicle according to an X-axis coordinate difference value, which is a difference value between the position of the moving object and the position of the unmanned following vehicle in a lateral direction.

Abstract: A time of flight (ToF) system comprises three photoemitters, a photosensor, and a controller. The first photoemitter transmits light onto objects at first height, the second photoemitter onto objects at second, lower height, and the third photoemitter onto objects at third, lowest height. The controller causes one of the photoemitters to transmit modulated light and the photosensor to receive reflections from the scene. The controller determines a depth map for the corresponding height based on phase differences between the transmitted and reflected light. In some examples, the ToF system is included in an autonomous robot's navigation system. The navigation system identifies overhanging objects at the robot's top from the depth map at the first height, obstacles in the navigation route from the depth map at the second height, and cliffs and drop-offs in the ground surface in front of the robot from the depth map at the third height.

Abstract: Disclosed are methods and devices related to autonomous driving. In one aspect, a method is disclosed. The method includes determining three-dimensional bounding indicators for one or more first objects in road target information captured by a light detection and ranging (LIDAR) sensor; determining camera bounding indicators for one or more second objects in road image information captured by a camera sensor; processing the road image information to generate a camera matrix; determining projected bounding indicators from the camera matrix and the three-dimensional bounding indicators; determining, from the projected bounding indicators and the camera bounding indicators, associations between the one or more first objects and the one or more second objects to generate combined target information; and applying, by the autonomous driving system, the combined target information to produce a vehicle control signal.

Abstract: A method, apparatus and system are provided for operating one or more drones in a building. In the context of a method, information is determined that includes at least one of real time information or predictive information. The real time information is indicative of a position of at least one individual in the building, while the predictive information is indicative of a predicted location of the at least one individual in the building at a certain time. The method also includes controlling the one or more drones in the building according to the at least one of the real time information or the predictive information to avoid the at least one individual while the drone is performing a task.

Abstract: A method for vehicle control. The method includes a step of reading in a camera signal, a step of executing a semantic segmentation, and a step of ascertaining a control signal. The camera signal represents an optically detected image of a roadway to be driven by a vehicle. In the step of executing, the semantic segmentation of the image represented by the camera signal is executed, to detect a free area ahead of the vehicle from the image as a drivable road section. In addition, a roadway signal representing the detected drivable road section is provided. In the step of ascertaining, the control signal for activating at least one vehicle component is ascertained, using the roadway signal.

Abstract: A method for creating a map by a moving robot includes receiving sensor data regarding a distance to an external object through a distance measurement sensor, and creating a cell-based grid map based on the sensor data. Image processing is performed to distinguish between regions in the grid map and to create a boundary line between the regions. An optimal boundary line is selected from the one or more boundary lines, and a path to the optimal boundary line is planned. The grid map is updated while the moving robot is moving along the path such that the map may be automatically created.

Abstract: An autonomous vehicle system removes ephemeral points from lidar samples. The system receives a plurality of light detection and ranging (lidar) samples captured by a lidar sensor. Along with the lidar samples, the system receives an aligned pose and an unwinding transform for each of the lidar samples. The system determines one or more occupied voxel cells in a three-dimensional (3D) space using the lidar samples, their aligned poses, and their unwinding transforms. The system identifies occupied voxel cells representative of noise associated with motion of an object relative to the lidar sensor. The system filters the occupied voxel cells by removing the cells representative of noise. The system inputs the filtered occupied voxel cells in a 3D map comprising voxel cells, e.g., during the map generation and/or a map update.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using surfels for vehicle localization. One of the methods includes obtaining surfel data comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment, and each surfel has associated data that comprises a stability measure, wherein the stability measure characterizes a permanence of a surface represented by the surfel; obtaining sensor data for a plurality of locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining a plurality of high-stability surfels from the plurality of surfels in the surfel data; and determining a location in the environment of the first vehicle using the plurality of selected high-stability surfels and the sensor data.

Abstract: An approach is provided for mapping based on pedestrian type. The approach, for instance, involves processing image data to a determine the pedestrian type of at least one pedestrian depicted in the image data. The approach also involves determining a classification of a geographic zone based on the detected pedestrian type. The approach further involves generating a digital map representation of the geographic zone based on the classification.

Abstract: Example moveable mounting structures and methods are described. In one implementation, an unmanned aerial vehicle (UAV) includes a body and a moveable mounting structure coupled to the body. The moveable mounting structure can move between a stowed position and a deployed position without interfering with a payload carried by the UAV. A camera is mounted to the moveable mounting structure.

Abstract: A nighttime delivery system is configured to provide delivery of packages during night hours, in low or no-light conditions using a multi-mode autonomous and remotely controllable delivery robot. The system involves the use of a baseline lighting system onboard the robot chassis, with variable light intensity from single flash and floodlighting, to thermal imaging using infrared cameras. The lighting system changes according to various environmental factors, and may utilize multi-mode lighting techniques to capture single-shot images with a flash light source to aid in the robot's perception when the vehicle is unable to navigate using infrared (IR) images.

Abstract: A drive unit of a robotic vehicle including a top surface having a mounting interface to interchangeably couple with multiple different modular payload structures configured to transport items in a facility, workspace or inventory management environment. The mounting interface is configured to securely engage with a mounting portion of the variety of different payload structures to enable a versatile exchange of the payload structure for different conveyance applications. The drive unit includes an electrical interface to communicatively couple with the modular payload structures. The drive unit is configured to use data communicated via the electrical coupling and interface to identify a type of modular payload structure that is mechanically coupled to the mounting interface and implement a motion profile (e.g., speed and acceleration parameters) associated with the identified modular payload structure.

Abstract: Aircraft systems and methods that determine, based on location data from a navigation system, whether conditions have been met enabling arming of an approach mode of an autopilot system. The systems and methods, when the one or more conditions enabling arming of the approach mode are determined to be met, start a first timer of a first period of time and, at the same time, provide a first message to alert a pilot to arm the approach mode via manual input to a user interface. When a determination has been made that the approach mode continues to have not been armed via manual input to the user interface, the approach mode is automatically armed.

Abstract: A system for both onboard piloting and remote piloting an aircraft having thereon at least one onboard pilot includes an aircraft having an onboard flight control system, onboard flight control devices, and onboard condition indicators mounted proximal the pilot seat. A remote flight control system, in electronic communication with the onboard flight control system, includes remote flight control devices and remote condition indicators. At least in a remote piloting mode, the onboard flight control system transmits flight and aircraft condition-related signals to the remote flight control system, receives flight control signals from the remote flight control system, and actuates, according to the flight control signals received from the remote flight control system, onboard actuators thereby remote piloting the aircraft. In the remote piloting mode, the aircraft may be cooperatively piloted by a remote pilot and at least one alert onboard pilot as another onboard pilot rests.

Abstract: A robot repositioning method is provided. A position deviation caused by excessive accumulation of a walking error of a robot may be corrected to implement repositioning by taking a path that the robot walks along an edge of an isolated object as a reference, so that the positioning accuracy and walking efficiency of the robot during subsequent navigation and walking are improved.

Abstract: 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.