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: A controlling method for an artificial intelligence moving robot according to an aspect of the present disclosure includes: checking nodes within a predetermined reference distance from a node corresponding to a current position; determining whether there is a correlation between the nodes within the reference distance and the node corresponding to the current position; determining whether the nodes within the reference distance are nodes of a previously learned map when there is no correlation; and registering the node corresponding to the current position on the map when the nodes within the reference distance are determined as nodes of the previously learned map, thereby being able to generate a map in which the environment of a traveling section and environmental changes are appropriately reflected.

Abstract: The present disclosure relates to a method for performing simultaneous localization and mapping (SLAM) with respect to a salient object in an image, a robot and a cloud server for implementing such method. According to an embodiment of the present disclosure, a robot includes a camera sensor configured to capture one or more images for the robot to perform the SLAM with respect to a salient object for estimating a location of the robot within the space, a map storage configured to store the information for the robot to perform the SLAM, and a controller that is configured to: detect an object from the captured image; select, as a specific salient object for identifying the space, the detected object verified as corresponding to the specific salient object; and store, in the map storage, the selected specific salient object and coordinate information related to the selected specific salient object.

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 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: A main server for controlling laser irradiation of a movement path of a robot, the main server including a communication unit configured to communicate with a camera module and a laser irradiation module; and a controller configured to: receive, via the communication unit, an image of a robot captured by the camera module, identify a location of the robot in the image captured by the camera module, generate a movement path of the robot based on sensing information, and transmit, via the communication unit, movement path information to the laser irradiation module for outputting the movement path to a vicinity of the robot with a laser for the robot to follow, in which the sensing information includes first information about an obstacle sensed by the camera module or second information about the obstacle sensed by the laser irradiation module.

Abstract: Provided are a method for drawing a map of a specific area, a robot and an electronic device that implement such a method. According to the present disclosure, the method, the robot, and the electronic device improve quality of the drawn map by moving the robot so that loop closing is performed many times when the map is drawn. To this end, an accumulated moving distance from a first position and a second position of the robot moving in the specific area is measured, and when the loop closing is not performed at the second position, whether the loop closing is required at the second position is determined based on the measured accumulated moving distance and information on matching of feature points at the second position, and a movement of the robot is controlled based on a result of the determination.

Abstract: A mobile robot includes a traveling unit configured to move a main body, a LiDAR sensor configured to acquire geometry information, a camera sensor configured to acquire an image of the outside of the main body, and a controller. The controller generates odometry information based on the geometry information acquired by the LiDAR sensor. The controller determines a current location of the mobile robot by performing feature matching between images acquired by the camera sensor based on the odometry information. The controller combines the information obtained by the camera sensor and the LiDAR sensor to accurately determine the current location of the mobile robot.

Abstract: A mobile robot and a method of controlling the mobile robot are disclosed. The method includes acquiring an image of an inside of a traveling zone. The method further includes performing a point-based feature point extraction by extracting a first feature point from the acquired image. The method also includes performing a block-based feature point extraction by dividing the acquired image into blocks having a predetermined size and extracting a second feature point from each of the divided block-unit images. The method also includes determining the current location by performing a point-based feature point matching using the first feature point and performing a block-based feature point using the second feature point. The method also includes storing the determined current location in association with the first feature point and the second feature point in a map.

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: 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: A robot and/or a computer system may be used to generate position information of the robot operating in an operating space that is subdivided into a plurality of cells. The robot includes a sensor configured to acquire data related to the operating space. A memory of the robot and/or the computer server may store information regarding at least two of the cells and an indicator of a position information acquisition technique used to generate position information of the robot in a cell. The controller may be configured to acquire information related to a cell that the robot is positioned in and generate the position information of the robot based on the position information acquisition technique, the sensor data, and the cell 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: A method of controlling a moving-robot includes creating a plurality of maps having different generation-time information through a plurality of driving processes; choosing any one of the plurality of maps according to a certain map choosing algorithm based on current time information and the generation-time information, and attempting location recognition based on the chosen map.

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 robot according to one embodiment may include a storage configured to store a map of a space in which the robot operates, an input interface configured to obtain at least one image of a surrounding environment of the robot, and at least one processor configured to estimate a first position of the robot based on the at least one image obtained by the input interface, determine candidate nodes in the map of the space based on the first position of the robot, and estimate at least one of a second position of the robot or a pose of the robot based on the determined candidate nodes. In a 5G environment connected for the Internet of Things, embodiments may be implemented by executing an artificial intelligence algorithm and/or machine learning algorithm.

Abstract: A controlling method for an artificial intelligence moving robot according to an aspect of the present disclosure includes: moving based on a map including a plurality of regions; acquiring images from the plurality of regions through an image acquisition unit during the moving; extracting region feature information based on the acquired image; and storing the extracted region feature information in connection with position information when an image is acquired.