Lg Electronics Inc.

Hyoungrock Kim

JungSik Kim

Joongtae Park

Gyuho Eoh

Dong Ki Noh

Minjung Kim

Sookhyun Yang

Moonsik Nam

Donghak Lee

Yunmi Kwon

Seungmin Baek

Sunho Yang

Yeonsoo Kim

Haemin Choi

Hyoung Seok Kim

Kanguk Kim

Sang Ki Kim

Jinuh Joo

Kang Uk Kim

Disclosed herein are a method of updating a map in fusion SLAM and a robot implementing the same, the robot, which updates a map in fusion SLAM using two types of sensors, configured to update a first map with first type information acquired by a first sensor and to estimate a current position of the robot using second type information acquired by a second sensor.

Method of updating map in fusion SLAM and robot implementing same

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.

Robot generating map based on multi sensors and artificial intelligence and moving based on map

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.

Localization of robot

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.

Mobile robot for recognizing queue and operating method of mobile robot

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.

Mobile robot operation method and mobile robot

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.

Robot generating map and configuring correlation of nodes based on multi sensors and artificial intelligence, and moving based on map, and method of generating map

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.

Method of localization by synchronizing multi sensors and robot implementing same

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.

Method of localization using multi sensor and robot implementing same

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.

Robot navigating through waypoints based on obstacle avoidance and method of robot's navigation

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.

Method of performing cloud slam in real time, and robot and cloud server for implementing the same

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.

Method of redefining position of robot using artificial intelligence and robot of implementing thereof

Disclosed is a drone. The present invention includes a plurality of propellers creating a lift to prevent inclination and overturn of the drone due to a lift difference generated from uneven ground, a power driving unit providing a rotation power to each of a plurality of the propellers, a ground sensing unit measuring a distance to a first region of the ground and a shape of the first region, and a controller controlling the power driving unit to differentiate rotation ratios of a plurality of the propellers based on the measured distance and shape if receiving an input signal for landing at the first region.

Drone

A robot cleaner including a driving unit configured to move the robot cleaner; a charging unit disposed on an upper surface of the robot cleaner, and configured to wirelessly charge a mobile terminal; and a control unit configured to control the driving unit to move the robot cleaner to a position of the mobile terminal corresponding to a sensed preset signal, control the robot cleaner to execute a wireless charging of the mobile terminal placed on the charging unit according to a charging command, and output a feedback signal when the wireless charging of the mobile terminal is completed.

Growing community of inventors

Seoul, South Korea

Hyoungrock Kim