Abstract
Clustering plays an important role in processing light detection and ranging points in the autonomous perception tasks of robots. Clustering usually occurs near the start of processing three-dimens...
Highlights
The application of light detection and ranging (LiDAR) has become more widespread in autonomous navigation systems[1,2,3,4,5,6,7] and three-dimensional (3D) environment reconstruction systems.[8,9,10,11,12] In both areas, clustering as a preprocessing step of 3D point clouds is very important because it directly affects the accuracy of object classification and dynamic object detection
Clustering can result in errors in object classification and dynamic object detection since object classification is usually executed after clustering according to the features of each cluster in order to recognize the category types to which they belong
A ground extraction method using scan line and a clustering method based on range image combining Density-based spatial clustering of application with noise (DBSCAN) were proposed for clustering of 3D points
Summary
The application of light detection and ranging (LiDAR) has become more widespread in autonomous navigation systems[1,2,3,4,5,6,7] and three-dimensional (3D) environment reconstruction systems.[8,9,10,11,12] In both areas, clustering as a preprocessing step of 3D point clouds is very important because it directly affects the accuracy of object classification and dynamic object detection. Many clustering algorithms[13,14] proposed for processing image can be used for processing 3D LiDAR points since the principle of clustering is similar that they all aim at grouping points with similar features of a given data set. Since pixels belonging to one object in an image have close indices, the feature of image accelerates the speed to find similar elements For points, they are spatially distributed, accessing all the points in the data set is necessary to find the similar points if no measurements are taken. A range image-based DBSCAN clustering method is proposed. It employs a two-dimensional (2D) range image in which each pixel stores the information of the corresponding point.
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