Abstract: A preconditioned conjugate gradient (PCG) solver, embedded in an electronic device to perform a simultaneous localization and mapping (SLAM) operation, includes an image database, a factor graph database, and a back-end processor, wherein the back-end processor is configured to receive an image from the image database to perform re-localization, receive, from the factor graph database, data for calculating six degrees of freedom (DoF)-related components, construct a matrix including the six degrees of freedom-related components based on the received data, and load and rearrange the matrix and a vector, to perform calculation on each block of each row of the matrix and the vector, then output first data, and shift second data to a location of the first data.

Abstract: A depth image generation method includes obtaining a first depth image, based on a binocular image, obtaining a second depth image, using a depth camera, and obtaining a final depth image by performing image fusion on the obtained first depth image and the obtained second depth image.

Abstract: Provided is a method of generating a computer-generated hologram (CGH), the method including obtaining complex data including amplitude data of object data and phase data of the object data corresponding to a spatial light modulator (SLM) plane by propagating the object data from an image plane to the SLM plane, encoding the complex data into encoded amplitude data, and generating a CGH based on the object data including the encoded amplitude data.

Abstract: An electronic device includes a communication interface and a processor, wherein the processor receives a first image predistorted and rendered at a first time, from an external device through the communication interface, calculates a pixel shift for each pixel of the received first image at a second time that is different from the first time, generates a second image by reprojecting the first image based on the calculated pixel shift, and transmits the generated second image to the external device through the communication interface.

Abstract: An apparatus for accelerating simultaneous localization and mapping (SLAM) includes a SLAM processor including a front-end processor and a back-end processor. The front-end processor is configured to track a position of a first feature, among features extracted from a first frame, in a second frame subsequent to the first frame, and the back-end processor is configured to obtain a first measurement regarding a map point and a camera pose of the first feature based on the position of the first feature in the second frame tracked by the front-end processor, compute elements affecting an optimization matrix in relation to the first measurement, among elements of a Hessian matrix regarding the map point and the camera pose, and accumulate the computed elements in the optimization matrix used to perform an optimization operation with respect to states of the map point and the camera pose.

Abstract: An augmented reality (AR) device and a method of predicting a pose in the AR device is provided. In the augmented reality (AR) device inertial measurement unit (IMU) values corresponding to the movement of the AR device are obtained at an IMU rate, intermediate 6-degrees of freedom (6D) poses of the AR device are estimated based on the IMU values and images around the AR device via a visual-inertial simultaneous localization and mapping (VI-SLAM) module, and a pose prediction model for predicting relative 6D poses of the AR device is generated by performing learning by using a deep neural network.

Abstract: A simultaneous localization and mapping device is provided. The device includes an image obtaining device configured to capture color images and depth images of a surrounding environment; an initial pose estimating device configured to estimate an initial pose based on the color images and the depth images; a map constructing device configured to construct a three-dimensional map based on the depth images and the color images; and a pose determining device configured to determine a final pose based on the initial pose and the three-dimensional map.

Abstract: An augmented reality (AR) device and a method of predicting a pose in the AR device is provided. In the augmented reality (AR) device inertial measurement unit (IMU) values corresponding to the movement of the AR device are obtained at an IMU rate, intermediate 6-degrees of freedom (6D) poses of the AR device are estimated based on the IMU values and images around the AR device via a visual-inertial simultaneous localization and mapping (VI-SLAM) module, and a pose prediction model for predicting relative 6D poses of the AR device is generated by performing learning by using a deep neural network.

Abstract: A method of correcting image latency in implementing augmented reality includes receiving a first image including frames, in which a time point and a pose of an electronic apparatus for each frame are recorded, from an external apparatus; rendering the first image; receiving first data corresponding to a first time point of a first frame; receiving second data corresponding to a second time point of a second frame; calculating a respective pose of the electronic apparatus for each scan line of a second image based on the first data and the second data; calculating a pixel shift for each pixel in each scan line based on the respective pose calculated for each scan line; generating the second image by correcting the first image based on the pixel shift; and transmitting the second image to the external apparatus.

Abstract: An electronic device includes a communication interface and a processor, wherein the processor receives a first image predistorted and rendered at a first time, from an external device through the communication interface, calculates a pixel shift for each pixel of the received first image at a second time that is different from the first time, generates a second image by reprojecting the first image based on the calculated pixel shift, and transmits the generated second image to the external device through the communication interface.

Abstract: Provided is a processor configured to compute elements affecting an optimization matrix in connection with a first measurement, among elements of a Hessian matrix, instead of generating a whole Hessian matrix for a map point and a camera pose based on all measurements, and accumulate the computed elements over the optimization matrix used to perform optimization operations in relation to states of the map point and the camera pose.

Abstract: Provided is an accelerator provided in an electronic device and configured to perform simultaneous localization and mapping (SLAM), the accelerator including a factor graph database, a memory, and a back-end processor, wherein the back-end processor is configured to receive a first piece of data corresponding to map points and camera positions from the factor graph database, convert the received first piece of data into a matrix for the map points and a matrix for the camera positions, store, in the memory, results obtained by performing an optimization calculation on the matrix for the map points and a matrix for at least one camera position, among the camera positions, corresponding to the map points, and obtain a second piece of data optimized with respect to the first piece of data based on the results stored in the memory.

Abstract: An augmented reality (AR) device and a method of predicting a pose in the AR device is provided. In the augmented reality (AR) device inertial measurement unit (IMU) values corresponding to the movement of the AR device are obtained at an IMU rate, intermediate 6-degrees of freedom (6D) poses of the AR device are estimated based on the IMU values and images around the AR device via a visual-inertial simultaneous localization and mapping (VI-SLAM) module, and a pose prediction model for predicting relative 6D poses of the AR device is generated by performing learning by using a deep neural network.

Abstract: Provided are non-uniform light-emitting lidar (light detection and ranging) apparatuses and autonomous robots including the same. A lidar apparatus may include a light source configured to emit light, an optical unit arranged on an optical path of light emitted from the light source and configured to change an optical profile of the light to be non-uniform, and a 3D sensor configured to sense location of an object by receiving reflection light from the object.

Abstract: A method of correcting image latency in implementing augmented reality includes receiving a first image including frames, in which a time point and a pose of an electronic apparatus for each frame are recorded, from an external apparatus; rendering the first image; receiving first data corresponding to a first time point of a first frame; receiving second data corresponding to a second time point of a second frame; calculating a respective pose of the electronic apparatus for each scan line of a second image based on the first data and the second data; calculating a pixel shift for each pixel in each scan line based on the respective pose calculated for each scan line; generating the second image by correcting the first image based on the pixel shift; and transmitting the second image to the external apparatus.

Abstract: Provided is a method of generating a computer-generated hologram (CGH), the method including obtaining complex data including amplitude data of object data and phase data of the object data corresponding to a spatial light modulator (SLM) plane by propagating the object data from an image plane to the SLM plane, encoding the complex data into encoded amplitude data, and generating a CGH based on the object data including the encoded amplitude data.

Abstract: An augmented reality (AR) device and a method of predicting a pose in the AR device is provided. In the augmented reality (AR) device inertial measurement unit (IMU) values corresponding to the movement of the AR device are obtained at an IMU rate, intermediate 6-degrees of freedom (6D) poses of the AR device are estimated based on the IMU values and images around the AR device via a visual-inertial simultaneous localization and mapping (VI-SLAM) module, and a pose prediction model for predicting relative 6D poses of the AR device is generated by performing learning by using a deep neural network.

Abstract: A preconditioned conjugate gradient (PCG) solver, embedded in an electronic device to perform a simultaneous localization and mapping (SLAM) operation, includes an image database, a factor graph database, and a back-end processor, wherein the back-end processor is configured to receive an image from the image database to perform re-localization, receive, from the factor graph database, data for calculating six degrees of freedom (DoF)-related components, construct a matrix including the six degrees of freedom-related components based on the received data, and load and rearrange the matrix and a vector, to perform calculation on each block of each row of the matrix and the vector, then output first data, and shift second data to a location of the first data.

Abstract: Provided are non-uniform light-emitting lidar (light detection and ranging) apparatuses and autonomous robots including the same. A lidar apparatus may include a light source configured to emit light, an optical unit arranged on an optical path of light emitted from the light source and configured to change an optical profile of the light to be non-uniform, and a 3D sensor configured to sense location of an object by receiving reflection light from the object.

Abstract: An augmented reality (AR) device and a method of predicting a pose in the AR device is provided. In the augmented reality (AR) device inertial measurement unit (IMU) values corresponding to the movement of the AR device are obtained at an IMU rate, intermediate 6-degrees of freedom (6D) poses of the AR device are estimated based on the IMU values and images around the AR device via a visual-inertial simultaneous localization and mapping (VI-SLAM) module, and a pose prediction model for predicting relative 6D poses of the AR device is generated by performing learning by using a deep neural network.