Abstract: Proposed is a calibration method for a lidar and an IMU using features according to the shape of a specific object included in point cloud data. The method may include placing point cloud data acquired by a lidar mounted on a vehicle on a predefined world coordinate system, by a data generator, extracting a region to be used for calibration from the placed point cloud data, by the data generator, identifying at least one object included in the extracted region, by the data generator, and performing calibration on the point cloud data by fitting point cloud included in the at least one identified object to a pre-stored model, by the data generator. The present method is a technology developed with support from the Ministry of Land/KAIA (Project No. RS-2021-KA160637).

Abstract: A method of lightweighting an artificial intelligence model can increase inference speed while maintaining accuracy of the artificial intelligence model for detecting objects in an image captured by a camera as much as possible. The method may include the steps of: pruning an artificial intelligence model machine-learned using a first data set, by a data processing device; quantizing the pruned artificial intelligence model, by the data processing device; and learning the artificial intelligence model by imitating another artificial intelligence model previously trained using a second data set including a larger amount of data than the first data set, by the data processing device.

Abstract: A building modeling method may include separating, by a data generating device, a point cloud corresponding to a ground and a point cloud corresponding to a non-ground from aerial point cloud data acquired from a LiDAR mounted on a flight device, classifying, by the data generating device, a point cloud corresponding to a roof of a building among the point clouds corresponding to the non-ground, and modeling, by the data generating device, the building based on the classified point cloud. The present method is technology developed with the support of the Ministry of Trade, Industry and Energy/Korea Planning & Evaluation Institute of Industrial Technology (Task No. 20022003/Project Name-Automotive Industry Technology Development Project/Task Name-Development of industrial autonomous driving and stability securing technology based on vertical and horizontal linkage).

Abstract: Proposed is a method for facility management through text recognition, capable of detecting a facility from an image captured by a camera mounted on a vehicle that travels on the road and recognizing text written on the detected facility to identify the type of facility. The method for facility management includes identifying, by a data processing device, an object corresponding to a preset facility on an image captured by a camera, recognizing, by the data processing device, text included in the identified object, and identifying, by the data processing device, a type of facility corresponding to the identified object based on the recognized text. The present method is technology developed with support from the Ministry of Land, Infrastructure and Transport/Korea Agency for Land, Infrastructure and Transport Science and Technology Promotion (task number RS2021-KA160637).

Abstract: Proposed is a map updating method for updating a map using data acquired from other sensors for regions with low global positioning system (GPS) signal sensitivity on a precision road map generated based on GPS information. The method may include loading, by a data generating device, a reference map generated based on location information acquired by a global positioning system (GPS) device and first point cloud data acquired from a first light detection and ranging (LiDAR) and updating, by the data generating device, the reference map based on second point cloud data acquired from a second LiDAR mounted on a vehicle traveling on a path of the reference map.

Abstract: A noise removal method may include receiving, by a data processing device, point cloud data acquired from a LiDAR and an image captured by a camera, identifying, by the data processing device, a preset object from the image, deleting, by the data processing device, a point cloud corresponding to the object identified from the image from the point cloud data, and generating, by the data processing device, a map based on point cloud data from which the point cloud corresponding to the object was deleted. The present method is technology developed and supported by the Ministry of Trade, Industry and Energy/Korea Planning & Evaluation Institute of Industrial Technology (Task No. 20017992/Project Name-Excellent Company Research Institute Development Project (ATC+)/Task Name-Development of Real-time risk detection and map generating solution based on 3D scanning technology to ensure safety during autonomous driving).

Abstract: Proposed is a map updating method for updating a map using data acquired from other sensors for regions with low global positioning system (GPS) signal sensitivity on a precision road map generated based on GPS information. The method may include loading, by a data generating device, a reference map generated based on location information acquired by a global positioning system (GPS) device and first point cloud data acquired from a first light detection and ranging (LiDAR) and updating, by the data generating device, the reference map based on second point cloud data acquired from a second LiDAR mounted on a vehicle traveling on a path of the reference map.

Abstract: Proposed is a calibration method for a camera and a lidar using a calibration board having a specific pattern. The method may include extracting one frame for an image captured by a camera and point cloud data acquired by a lidar, by a data generator, identifying a feature point for a calibration board included in one extracted frame for the image and the lidar, by the data generator, and performing calibration for the camera and the lidar based on the identified feature point, by the data generator. The present method is a technology developed with support from the Ministry of Trade, Industry and Energy/Korea Planning and Evaluation Institute of Industrial Technology (Project No. 20022003/Project name-Automotive industry technology development project/Project name-Development of industrial autonomous driving and safety assurance technology based on longitudinal and lateral interconnection).

Abstract: Proposed is a calibration method for a lidar and an IMU using features according to the shape of a specific object included in point cloud data. The method may include placing point cloud data acquired by a lidar mounted on a vehicle on a predefined world coordinate system, by a data generator, extracting a region to be used for calibration from the placed point cloud data, by the data generator, identifying at least one object included in the extracted region, by the data generator, and performing calibration on the point cloud data by fitting point cloud included in the at least one identified object to a pre-stored model, by the data generator. The present method is a technology developed with support from the Ministry of Land/KAIA (Project No. RS-2021-KA160637).

Abstract: A calibration method for multiple LIDARS may comprise the steps of acquiring, by a data creation device, first point cloud data from a first LIDAR in order to create a reference map, a plurality of second point cloud data acquired from a plurality of second LIDARs mounted on a vehicle traveling on a path on the reference map and specification values for the first LIDAR and the plurality of second LIDARs; performing, by the data creation device, preprocessing on the first point cloud data and the plurality of second point cloud data; and performing, by the data creation device, calibration on the preprocessed first point cloud data and plurality of second point cloud data.

Abstract: Proposed is a visual mapping method for generating a feature map by mapping a feature point of an image captured by a camera to point cloud data acquired by a lidar. The method may include generating a first feature map based on point cloud data obtained from a lidar and an image captured from a camera, by a data generator, and generating a third feature map by mapping the first feature map on a second feature map generated through pre-stored point cloud data, by the data generator. The present method is a technology developed with support from the Ministry of Trade, Industry and Energy/Korea Planning and Evaluation Institute of Industrial Technology (Project No. 201792/Business name-Excellent enterprise research center promotion project (ATC+)/Project name-Development of real-time risk detection and mapping solution based on 3D scanning technology to ensure safety in autonomous driving).

Abstract: A lane extraction method uses projection transformation of a 3D point cloud map, by which the amount of operations required to extract the coordinates of a lane is reduced by performing deep learning and lane extraction in a two-dimensional (2D) domain, and therefore, lane information is obtained in real time. In addition, black-and-white brightness, which is most important information for lane extraction on an image, is substituted by the reflection intensity of a light detection and ranging (LiDAR) sensor so that a deep learning model capable of accurately extracting a lane is provided. Therefore, reliability and competitiveness is enhanced in the field of autonomous driving, the field of road recognition, the field of lane recognition, and the field of HD road maps for autonomous driving, and the fields similar or related thereto, and more particularly, in the fields of road recognition and autonomous driving using LiDAR.

Abstract: Proposed is a method for estimating the distance to and the location of an autonomous vehicle by using a mono camera, and more particularly, a method that enables information necessary for autonomous travel to be efficiently acquired using a mono camera and LiDAR. In particular, the method can acquire sufficiently reliable information in real time without using expensive equipment, such as a high-precision GPS receiver or stereo camera, required for autonomous travel. Consequently, the method may be widely used in ADAS, such as for semantic information recognition for autonomous travel, estimation of the location of an autonomous vehicle, calculation of vehicle-to-vehicle distance, or the like, even without the use of GPS, and furthermore, a camera capable of performing the same functions can be developed by developing software through the use of the corresponding data.

Abstract: The present invention relates to a method for predicting a state of an object on the basis of dynamic image data and a computing device performing same, the method enabling initial dynamic image data and delay image data to be predicted by performing learning on the basis of dynamic image data captured at a time point when both blood flow image information and disease-specific biological information are included, and furthermore, enabling blood flow image information and disease-specific biological information of the object to be provided.

Abstract: A synchronization device according to an embodiment may include a Light Detection and Ranging (LiDAR), a plurality of cameras, and a synchronization control unit, and a method of synchronizing a rotating LiDAR and a camera according to an embodiment may include the steps of: outputting a sample according to a predetermined output period while rotating, by the LiDAR; acquiring the output sample, and deriving a trigger timing, which is an expected time for the LiDAR to rotate from a current position to a trigger target on the basis of the sample, by the synchronization control unit; transmitting a first trigger signal to the first camera and the second camera after a time of the trigger timing elapses, by the synchronization control unit; and additionally transmitting a second trigger signal only to the second camera after transmitting the first trigger signal, by the synchronization control unit.

Abstract: A trajectory correction method may include the steps of: correcting, by a map generation device, an error generated due to a change in position targeting 3D point cloud data collected in real time through a LiDAR, of which a position changes in real time; estimating, by the map generation device, an expected trajectory (odometry) related to the change in position on the basis of the corrected 3D point cloud data; applying, by the map generation device, curve fitting to the estimated expected trajectory; and mapping, by the map generation device, the expected trajectory before the curve fitting targeting the expected trajectory to which the curve fitting is applied.

Abstract: A rechargeable battery module including: a plurality of battery cells, each of the battery cells having a vent hole; a first holder accommodating one side of the battery cells; a second holder accommodating an opposite side of the battery cells, the second holder being coupled to the first holder with the battery cells therebetween; a first plate on one side of the second holder and an outlet group, the outlet group including a plurality of outlets at positions corresponding to the vent holes of a respective one of the battery cells; and a second plate on one side of the first plate and configured to intercept an emission discharged through the vent hole and the outlet group.

Abstract: Proposed is a GPS error correction method performed through comparison of three-dimensional HD-maps in a duplicate area, and more particularly, a method that can calculate a correction value for a GPS error by comparing three-dimensional HD-maps of a corresponding duplicate area when the duplicate area is generated on a GPS route in the process of acquiring raw data to be used in a HD-map for autonomous driving. Particularly, in the method, an accurate correction value for the GPS error can be calculated by comparing three-dimensional point cloud data acquired by utilizing basically installed LiDAR, an Inertial Measurement Unit (IMU) and the like, without using expensive equipment such as a plurality of high-precision GPS receivers, stereo cameras or the like.

Abstract: Proposed is a method of generating learning data for traffic facilities, which can facilitate recognition of traffic facilities. The method includes the steps of: collecting, by a learning data generation unit, traffic facility sample images; generating, by the learning data generation unit, at least one processed image by processing the collected traffic facility sample images to be recognized as images captured by a camera installed in a vehicle; and generating, by the learning data generation unit, learning data by inserting a background into the generated at least one processed image.

Abstract: Proposed is a method of generating a map using an aviation LiDAR for effectively generating a 3D map using point cloud data acquired from the LiDAR installed in an aviation device. The method includes the steps of: receiving, by a map generation unit, point cloud data acquired from a LiDAR installed in an aviation device; projecting, by the map generation unit, a top-view of the received point cloud data; and generating a map, by the map generation unit, by scanning the projected point cloud data in a direction perpendicular to the ground.