Abstract: A LIDAR data based object recognition apparatus merges segments over-segmented in a LIDAR data based segmentation process. The apparatus includes a segment generator generating a plurality of segments by grouping points acquired from a LIDAR sensor. A target segment selector selects a target segment that is a base for merging from the plurality of segments and a segment merging determination unit checks whether segments other than the target segment are mergeable segments and determines whether to merge the target segment and the mergeable segments based on attribute information of the target segment and the mergeable segments. A segment merger merges the target segment and the mergeable segments and outputs a merged segment.

Abstract: A method for clustering point cloud data includes the following steps of identifying a class of each point data of the point cloud data, the class assigned according to a semantic segmentation processing of the point cloud data, storing a plurality of point data of the point cloud data in virtual layers based on the class assigned to each point data, the virtual layers each associated with at least one class; and clustering the plurality of point data for each of the virtual layers.

Abstract: An embodiment LiDAR free space data generator includes a point filter module configured to select input points in a region of interest of an autonomous parking system from a three-dimensional (3D) point cloud obtained from a LiDAR, a two-dimensional (2D) grid map generation module configured to perform 2D grid-wise point processing on the selected input points to generate a first occupied grid feature map, and a multi-modal noise filter module configured to perform grid-level noise filtering, grid-level downsampling, or point-level noise filtering on the first occupied grid feature map to generate first LiDAR free space data.

Abstract: A LiDAR-based object detection method includes clustering a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points from among points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.

Abstract: An object detecting method of a vehicle LiDAR system may be disclosed. The object detecting method includes generating an overhead cluster corresponding to an object whose height from the ground may be equal to or larger than a reference height and a grounded cluster whose height from the ground may be smaller than the reference height, in point cloud data obtained by sensing an object; and comparing point data included in the overhead cluster and point data included in the grounded cluster, and removing the grounded cluster upon determining that an object corresponding to the corresponding grounded cluster does not exist.

Abstract: A method of tracking an object using a LiDAR sensor includes: acquiring a point cloud using each of LiDAR sensors and clustering the point cloud. The clustering includes generating a three-dimensional (3D) grid map using the point cloud acquired by each of the LiDAR sensors; labeling voxels present in the 3D grid map with one cluster using direction points of the voxels; and checking whether it is necessary to label the voxels with mutually different clusters.

Abstract: A method of tracking an object includes checking points generated by a LiDAR sensor to determine a first interpolation reference point and a second interpolation reference point when there are missing points being associated with the object, and generating interpolation points to replace the missing points between the first interpolation reference point and the second interpolation reference point.

Abstract: An object-tracking method using a LiDAR sensor according to an embodiment includes: generating a grid map based on a point cloud generated with respect to an object using a LiDAR sensor mounted to a vehicle traveling in an x-axis direction; performing static labeling when the absolute value of the y-coordinate of the center of each grid on the grid map in a y-axis direction that intersects the x-axis direction is less than a y-axis coordinate threshold value; and performing dynamic labeling when the absolute value is not less than the y-axis coordinate threshold value. Since both static labeling and dynamic labeling are utilized, it is possible to improve the performance of outputting information about an object that is elongated, thus improving the performance of an autonomous driving system, such as prediction of a travel route and map matching.

Abstract: An object-tracking apparatus using a LiDAR sensor of an embodiment includes: a LiDAR sensor configured to acquire a point cloud related to an object; a first clustering unit configured to primarily cluster the point cloud; and a second clustering unit configured to secondarily cluster the result of primary clustering in response to the time taken to perform primary clustering.

Abstract: An apparatus and a method for recognizing a high-elevation structure using a LiDAR sensor are provided. The apparatus includes an object-sensing unit configured to output a point cloud, acquired by sensing an object, as LiDAR data, a point-extracting unit configured to extract points associated with a high-elevation structure, which corresponds to an object having a height greater than or equal to a predetermined level, from among points included in the LiDAR data, and an information-generating unit configured to generate the contour of the high-elevation structure and estimate the location of the high-elevation structure using the extracted points and to output the generated contour and the estimated location as information representing the high-elevation structure.

Abstract: A LIDAR data based object recognition apparatus merges segments over-segmented in a LIDAR data based segmentation process. The apparatus includes a segment generator generating a plurality of segments by grouping points acquired from a LIDAR sensor. A target segment selector selects a target segment that is a base for merging from the plurality of segments and a segment merging determination unit checks whether segments other than the target segment are mergeable segments and determines whether to merge the target segment and the mergeable segments based on attribute information of the target segment and the mergeable segments. A segment merger merges the target segment and the mergeable segments and outputs a merged segment.

Abstract: A LIDAR data clustering apparatus, a system including the same, and a method thereof are provided. The apparatus includes a processor that is configured to generate a voxel map including at least one point data based on a LIDAR and remove a road surface point from the at least one point data. The processor also clusters the at least one point data. A storage of the apparatus is configured to store data obtained by the processor and an algorithm for driving the processor.