Lg Electronics Inc.

Samsung Electronics Co., Ltd.

JungSik Kim

Gyuho Eoh

Hyoungrock Kim

Joongtae Park

Jaehyung Lee

Kwangil Park

Youngdae Lee

Chulsung Park

Sungmin Yim

Duyeul Kim

Dong Ki Noh

Sookhyun Yang

Moonsik Nam

Minjung Kim

Jinuh Joo

Donghak Lee

Seungmin Baek

Sunho Yang

Jaehark Park

Namjin Kim

Taegil Cho

Eulpyo Hong

Byeongrim Jo

Gyunghwan Yuk

Honggul Jun

Sang Ki Kim

Yoonyoung Chang

Heuisik Seo

Sangho Yoon

Byunghoon Min

Hongsoo Park

Byungju Dan

Woosok Chang

Duksang 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

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.

Moving robot and traveling method thereof in corner areas

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

A semiconductor device includes a first die connected to a first channel, the first die comprising a first memory chip; and a second die connected to a second channel, the second die comprising a second memory chip, the first and second channels being independent of each other and a storage capacity and a physical size of the second die being the same as those of the first die. The first and second dies are disposed in one package, and the package includes an interconnection circuit disposed between the first die and the second die to transfer signals between the first memory chip and the second memory chip.

Semiconductor device having interconnection in package and method for manufacturing the same

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.

Method of performing simultaneous localization and mapping with respect to a salient object in an image

Disclosed herein are a method of identifying a dynamic obstacle and a robot implementing the same, wherein the robot configured to identify a dynamic obstacle may change mechanisms for identifying an obstacle in an image captured by a camera sensor in a first direction and for sensing the obstacle on the basis of a velocity of movement of the identified obstacle, a degree of congestion based on distribution of the obstacle and a velocity of movement of the robot, to generate a moving path of the robot.

Method of identifying dynamic obstacle and robot implementing same

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

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.

Method of tracking user position using crowd robot, tag device, and robot implementing thereof

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

Provided herein are a method of extracting a feature from an image using a laser pattern and an identification device and a robot including the same, and the identification device for extracting a feature from an image using a laser pattern, which includes a first camera coupled to a laser filter and configured to generate a first image including a pattern of a laser which is reflected from an object, a second camera configured to capture an area overlapping an area captured by the first camera to generate a second image, and a controller configured to generate a mask for distinguishing an effective area using the pattern included in the first image and extract a feature from the second image by applying the mask to the second image.

Method of extracting feature from image using laser pattern and device and robot of extracting feature thereof

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.

Method of estimating position in local area of large space and robot and cloud server implementing thereof

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.

Growing community of inventors

Seoul, South Korea

JungSik Kim