Abstract: The present disclosure relates to a moving robot that generates image information by accumulating results of sensing a corner area using a 3D camera sensor, and detects an obstacle by extracting an area for identifying the obstacle from the image information, and a traveling method thereof in corner areas.

Abstract: A robot may include an input interface configured to obtain an image of a surrounding environment of the robot, and at least one processor configured to rotate the robot in place for localization, and estimate at least one of a position or a pose in a space, based on a plurality of sequential images obtained by the input interface during the rotation of the robot. The position or the pose of the robot may be estimated based on inputting the plurality of sequential images obtained during the rotation of the robot into a trained model based on an artificial neural network. In a 5G environment connected for the Internet of Things, embodiments of the present disclosure may execute an artificial intelligence algorithm and/or a machine learning algorithm.

Abstract: A method for estimating a position of a robot in a local area of a large space and a robot and a cloud server that implement such method are provided. The robot includes a local area classifier configured to identify a local area of a plurality of local areas of the space in which the robot moves and a plurality of position estimators configured to estimate the position of the robot in the local area.

Abstract: The present disclosure relates to a moving robot that generates image information by accumulating results of sensing a corner area using a 3D camera sensor, and detects an obstacle by extracting an area for identifying the obstacle from the image information, and a traveling method thereof in corner areas.

Abstract: A robot may include an input interface configured to obtain an image of a surrounding environment of the robot, and at least one processor configured to rotate the robot in place for localization, and estimate at least one of a position or a pose in a space, based on a plurality of sequential images obtained by the input interface during the rotation of the robot. The position or the pose of the robot may be estimated based on inputting the plurality of sequential images obtained during the rotation of the robot into a trained model based on an artificial neural network. In a 5G environment connected for the Internet of Things, embodiments of the present disclosure may execute an artificial intelligence algorithm and/or a machine learning algorithm.

Abstract: A method for estimating a position of a robot in a local area of a large space and a robot and a cloud server that implement such method are provided. The robot includes a local area classifier configured to identify a local area of a plurality of local areas of the space in which the robot moves and a plurality of position estimators configured to estimate the position of the robot in the local area.

Abstract: Disclosed are a mobile terminal and controlling method thereof, by which saved image data can be effectively searched. The present invention includes a memory configured to store at least one image data, a touchscreen, and a controller outputting a thumbnail list for the stored image data on a 1st scale through the touchscreen, the controller receiving a scroll input to the outputted thumbnail list plural times, the controller, if the scroll input received plural times meets a scale change condition, changing the 1st scale of the outputted thumbnail list into a 2nd scale.

Abstract: Disclosed are a mobile terminal and controlling method thereof, by which saved image data can be effectively searched. The present invention includes a memory configured to store at least one image data, a touchscreen, and a controller outputting a thumbnail list for the stored image data on a 1st scale through the touchscreen, the controller receiving a scroll input to the outputted thumbnail list plural times, the controller, if the scroll input received plural times meets a scale change condition, changing the 1st scale of the outputted thumbnail list into a 2nd scale.