Abstract
Real-time acquisition and intelligent classification of pole-like street-object point clouds are of great significance in the construction of smart cities. Efficient point cloud processing technology in road scenes can accelerate the development of intelligent transportation and promote the development of high-precision maps. However, available algorithms have the problems of incomplete extraction and the low recognition accuracy of pole-like objects. In this paper, we propose a segmentation method of pole-like objects under geometric structural constraints. As for classification, we fused the classification results at different scales with each other. First, the point cloud data excluding ground point clouds were divided into voxels, and the rod-shaped parts of the pole-like objects were extracted according to the vertical continuity. Second, the regional growth based on the voxel was carried out based on the rod part to retain the non-rod part of the pole-like objects. A one-way double coding strategy was adopted to preserve the details. For spatial overlapping entities, we used multi-rule supervoxels to divide them. Finally, the random forest model was used to classify the pole-like objects based on local- and global-scale features and to fuse the double classification results under the different scales in order to obtain the final result. Experiments showed that the proposed method can effectively extract the pole-like objects of the point clouds in the road scenes, indicating that the method can achieve high-precision classification and identification in the lightweight data. Our method can also bring processing inspiration for large data.
Highlights
With the emergence of the concepts of the “smart city” and “twin city”, and the continued advancement of related research, how to carry out rapid data collection and environmental perception of urban scenes is becoming a research hotspot
The experimental results indicated that the method can quickly extract and accurately identify the pole-like objects of the road scene point clouds
Ground point filtering and point cloud downsampling can effectively reduce the computing amount of the computer, improve the efficiency of the program, and greatly reduce the time required for the implementation of the subsequent pole-like object extraction algorithm
Summary
With the emergence of the concepts of the “smart city” and “twin city”, and the continued advancement of related research, how to carry out rapid data collection and environmental perception of urban scenes is becoming a research hotspot. How to realize the rapid recognition of pole-like objects has great significance to the ubiquitous perception of the urban scene and the census of road resources [1,2,3]. Mobile lidar is a newer type of road environment information collection method that can quickly and efficiently obtain real-time access to roads and its auxiliary facilities as well as partial building facades. It can realize the synchronous acquisition of image data and point cloud data, and enormously enrich the
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