The present disclosure provides a work vehicle that allows detecting an error in an installation position of an antenna more flexibly than a conventional device. The work vehicle includes a control device 150. The control device 150 has a detection function F106, a calculation function F104, a calculation function F105, a calculation function F107, and an estimation function F110. The detection function F106 detects steady traveling based on a velocity, an acceleration, and an angular velocity of a vehicle. The calculation function F104 calculates a first vehicle direction based on installation information of a first antenna and a second antenna with respect to the vehicle. The calculation function F105 calculates a second vehicle direction based on a time change of position information of the first antenna when the steady traveling is detected. The calculation function F107 calculates a direction correction parameter for correcting the first vehicle direction based on the second vehicle direction. The estimation function F110 estimates a location and a posture of the vehicle based on the direction correction parameter and the first vehicle direction.

An integrated navigation method for a mobile vehicle is provided, which includes: acquiring a motion measurement of the mobile vehicle by using an inertial navigation element in the mobile vehicle and calculating a gesture parameter of the mobile vehicle based on the motion parameter; estimating, based on the gesture parameter, a motion state of the mobile vehicle in a real time manner by using a satellite navigation element in the mobile vehicle to obtain an error estimation value of the motion state, and correcting a motion parameter of the mobile vehicle based on the error estimation value of the motion state; and controlling an operation route of the mobile vehicle based on corrected navigation information.

A method of tracking a user position using a crowd robot, a tag device, and a robot implementing the same are disclosed, and the robot includes a controller, which cumulatively stores position information of a tag device, generates a moving route corresponding to the stored position information of the tag device, and corrects the position information of the tag device based on position estimation information of a crowd robot around the tag device sent from the tag device.

Disclosed is a transporting robot which executes a mounted artificial intelligence (AI) algorithm and/or machine learning algorithm and communicates with different electronic devices and external servers in a 5G communication environment. The transporting robot includes a wheel driver, a loading box, and a robot controller. The transporting robot is provided such that a transporting service using an autonomous robot may be provided.

The present invention relates to a mobile robot and a method for controlling the same. The present invention provides a mobile robot using the rotational force of at least three rotating members as a movement power source and a method for controlling the same, in which the mobile robot is controlled to effectively travel along a set curved driving path and not deviate from the set curved driving path, or to immediately return to the curved driving path when deviating from the set curved driving path.

A method comprising obtaining, from a sensor, depth data representing a target object; selecting a model to fit to the depth data; for each data point in the depth data: defining a ray from a location of the sensor to the data point; and determining an error based on a distance from the data point to the model along the ray; when the depth data does not meet a similarity threshold for the model based on the determined errors, selecting a new model and repeating the error determination for the depth data based on the new model; when the depth data meets the similarity threshold for the model, selecting the model as representing the target object; and outputting the selected model representing the target object.

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.

Provided are a robot cleaner including: a drive part configured to apply a driving force required to drive the robot cleaner; a sensor configured to obtain at least one of information about a travel state of the robot cleaner and information about surroundings of the robot cleaner; and a controller configured to determine at least one reference trajectory on a coordinate system of a cleaning area, and to compensate for a degree to which the robot cleaner is spaced apart from the reference trajectories based on at least one of a position and an direction of the robot cleaner, which are determined based on the at least one information obtained by the sensor, and a method for controlling a travel of the robot cleaner.

A method for specifying a cleaning area to a cleaning robot without an in-built map provides a hand-held mobile device capturing a two-dimensional code label arranged on a top of a cleaning robot parked on a charging base, and obtaining a positional relationship between the mobile device and the cleaning robot through the captured image. The cleaning robot is controlled to enter a cleaning mode under the guidance of the mobile device. With captured images, a user can specify an area within the environment for cleaning, and through a touch display screen can control the cleaning robot to go to the specified cleaning area for cleaning. The mobile device and the cleaning robot employing the method are also disclosed.

A method for ascertaining an initial pose of a vehicle using a control device. Measured data ascertained by a GNSS sensor system and/or an odometry sensor system are received and evaluated to ascertain an approximate pose of the vehicle with a margin of uncertainty. At least one trajectory of road users is extracted from a trajectory map for the ascertained margin of uncertainty. Test points are positioned along the extracted trajectory. An optimization algorithm is performed for each test point along the trajectory. The optimization algorithm ascertains poses having corresponding cost functions. A pose having the greatest cost function is determined as the initial pose of the vehicle from the poses ascertained by the optimization algorithm. A control device, a computer program, and a machine-readable storage medium are also provided.