Abstract: Disclosed herein is a robot generating a map based on multi sensors and artificial intelligence and moving based on the map, the robot according to an embodiment including a controller generating a pose graph that includes a LiDAR branch including one or more LiDAR frames, a visual branch including one or more visual frames, and a backbone including two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and generating orodometry information that is generated while the robot is moving between the frame nodes.

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: Disclosed is a moving robot capable of recognizing a waiting line and a method for controlling the same. One embodiment provides a method for operating a moving robot, the method comprising: starting moving from a predefined moving start point toward a predefined moving end point; acquiring a waiting line region image by photographing a predefined waiting line region during the moving; searching for an end point of a waiting line formed in the waiting line region using the waiting line region image; terminating the moving when the end point of the waiting line is detected; setting an operation mode based on a length of the waiting line calculated using the end point of the waiting line; and operating in the set operation mode while returning to the moving start point.

Abstract: A mobile robot operation method according to an aspect of the present invention includes: a step for receiving a guidance destination input; a step for generating a global path to the received guidance destination; a step for generating a left travel guideline and a right travel guideline on the left side and the right side of the generated global path; and a step for generating a local path within a travelable range between the left travel guideline and the right travel guideline. Accordingly, the robot operation method may generate a safe and optimal guidance path when providing a guidance service.

Abstract: Disclosed herein are a robot that generates a map and configures a correlation of nodes, based on multi sensors and artificial intelligence, and that moves based on the map, and a method of generating a map, and the robot according to an embodiment generates a pose graph comprised of LiDAR branch, visual branch, and backbone, and the LiDAR branch includes one or more of the LiDAR frames, the visual branch includes one or more of the visual frames, and the backbone includes two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and to generate a correlation between nodes in the pose graph.

Abstract: Disclosed herein are a method of localization by synchronizing multi sensors and a robot implementing the same. The robot according to an embodiment includes a controller that, when a first sensor acquires first type information, generates first type odometry information using the first type information, that, at a time point when the first type odometry information is generated, acquires second type information by controlling a second sensor and then generates second type odometry information using the second type information, and that the robot by combining the first type odometry information and the second type odometry information.

Abstract: Disclosed herein are a robot navigating based on obstacle avoidance and a navigation method. In the robot or the navigation method of the robot according to an embodiment, a navigation route may be generated on the basis of position information on a waypoint and on objects sensed by a sensor, such that the robot may move via one or more waypoints.

Abstract: Disclosed herein are a method of localization using multi sensors and a robot implementing the same, the method including sensing a distance between an object placed outside of a robot and the robot and generating a first LiDAR frame by a LiDAR sensor of the robot while a moving unit moves the robot, capturing an image of an object placed outside of the robot and generating a first visual frame by a camera sensor of the robot, and comparing a LiDAR frame stored in a map storage of the robot with the first LiDAR frame, comparing a visual frame registered in a frame node of a pose graph with the first visual frame, determining accuracy of comparison's results of the first LiDAR frame, and calculating a current position of the robot by a controller.

Abstract: Provided are a method of performing cloud simultaneous localization and mapping (SLAM), and a robot and a cloud server for performing the same, and a robot for performing cloud SLAM in real time includes a first sensor configured to acquire sensor data necessary to perform the SLAM, a map storage unit configured to store a map synchronized with a cloud server, a communication unit configured to transmit the sensor data, a feature, or a last frame to the cloud server and receive a local map patch or a global pose from the cloud server, and a control unit configured to generate the sensor data, the feature extracted from the sensor data, or the last frame and control movement of the robot using the local map patch or the global pose received from the cloud server.

Abstract: A method and a robot of redefining a position of a robot using artificial intelligence are disclosed, and the robot that redefines a position using artificial intelligence separately stores an entrance-allowable area and an entrance-unallowable area in a space in which the robot moves as a map and stores locating posts that are required for the robot to restore a position thereof.

Abstract: Disclosed herein are a robot that generates a map and configures a correlation of nodes, based on multi sensors and artificial intelligence, and that moves based on the map, and a method of generating a map, and the robot according to an embodiment generates a pose graph comprised of LiDAR branch, visual branch, and backbone, and the LiDAR branch includes one or more of the LiDAR frames, the visual branch includes one or more of the visual frames, and the backbone includes two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and to generate a correlation between nodes in the pose graph.

Abstract: Disclosed herein are a method of localization by synchronizing multi sensors and a robot implementing the same. The robot according to an embodiment includes a controller that, when a first sensor acquires first type information, generates first type odometry information using the first type information, that, at a time point when the first type odometry information is generated, acquires second type information by controlling a second sensor and then generates second type odometry information using the second type information, and that the robot by combining the first type odometry information and the second type odometry information.

Abstract: Disclosed herein is a robot generating a map based on multi sensors and artificial intelligence and moving based on the map, the robot according to an embodiment including a controller generating a pose graph that includes a LiDAR branch including one or more LiDAR frames, a visual branch including one or more visual frames, and a backbone including two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and generating orodometry information that is generated while the robot is moving between the frame nodes.

Abstract: Disclosed herein are a robot navigating based on obstacle avoidance and a navigation method. In the robot or the navigation method of the robot according to an embodiment, a navigation route may be generated on the basis of position information on a waypoint and on objects sensed by a sensor, such that the robot may move via one or more waypoints.

Abstract: A method and a robot of redefining a position of a robot using artificial intelligence are disclosed, and the robot that redefines a position using artificial intelligence separately stores an entrance-allowable area and an entrance-unallowable area in a space in which the robot moves as a map and stores locating posts that are required for the robot to restore a position thereof.

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: Disclosed herein are a method of localization using multi sensors and a robot implementing the same, the method including sensing a distance between an object placed outside of a robot and the robot and generating a first LiDAR frame by a LiDAR sensor of the robot while a moving unit moves the robot, capturing an image of an object placed outside of the robot and generating a first visual frame by a camera sensor of the robot, and comparing a LiDAR frame stored in a map storage of the robot with the first LiDAR frame, comparing a visual frame registered in a frame node of a pose graph with the first visual frame, determining accuracy of comparison's results of the first LiDAR frame, and calculating a current position of the robot by a controller.

Abstract: Disclosed is a moving robot capable of recognizing a waiting line and a method for controlling the same. One embodiment provides a method for operating a moving robot, the method comprising: starting moving from a predefined moving start point toward a predefined moving end point; acquiring a waiting line region image by photographing a predefined waiting line region during the moving; searching for an end point of a waiting line formed in the waiting line region using the waiting line region image; terminating the moving when the end point of the waiting line is detected; setting an operation mode based on a length of the waiting line calculated using the end point of the waiting line; and operating in the set operation mode while returning to the moving start point.

Abstract: A mobile robot operation method according to an aspect of the present invention includes: a step for receiving a guidance destination input; a step for generating a global path to the received guidance destination; a step for generating a left travel guideline and a right travel guideline on the left side and the right side of the generated global path; and a step for generating a local path within a travelable range between the left travel guideline and the right travel guideline. Accordingly, the robot operation method may generate a safe and optimal guidance path when providing a guidance service.

Abstract: A guidance robot comprising: a map management module configured to store map data; a camera configured to capture an image; a communication interface configured to transmit or receive data; an imaging processor configured to process an image; a display configured to display the image processed by the imaging processor; a motor configured to generate a force to move the guidance robot; and a controller configured to control an operation of the guidance robot, wherein in respond to receiving a road guidance request signal, the controller is to control the camera, the display to display a real-time image of a region of a movement path captured by the camera while moving along a determined movement path from a current position to a destination position, based on the map data.