Vehicle speed is a key variable for the calibration, validation, and improvement of traffic emission and air quality models. Lidar technologies have significant potential in vehicle tracking by scanning the surroundings in 3-D frequently, hence can be used as traffic flow monitoring sensors for accurate vehicle counting and speed estimation. However, the characteristics of lidar-based vehicle tracking and speed estimation, such as attainable accuracy, remain as open questions. This research therefore proposes a tracking framework from roadside lidar to detect and track vehicles with the aim of accurate vehicle speed estimation. Within this framework, on-road vehicles are first detected from the observed point clouds, after which a centroid-based tracking flow is implemented to obtain initial vehicle transformations. A tracker, utilizing the unscented Kalman Filter and joint probabilistic data association filter, is adopted in the tracking flow. Finally, vehicle tracking is refined through an image matching process to improve the accuracy of estimated vehicle speeds. The effectiveness of the proposed approach has been evaluated using lidar data obtained from two different panoramic 3-D lidar sensors, a RoboSense RS-LiDAR-32 and a Velodyne VLP-16, at a traffic light and a road intersection, respectively, in order to account for real-world scenarios. Validation against reference data obtained by a test vehicle equipped with accurate positioning systems shows that more than 94% of vehicles could be detected and tracked, with a mean speed accuracy of 0.22 m/s.

Three-dimensional (3-D) modeling of indoor environment plays an important role in various applications such as indoor navigation, Building Information Modeling (BIM), interactive visualization, etc. While automated reconstruction of 3-D models from point clouds is receiving more and more attention. Indoor modeling remains a challenging task in terms of dealing with the complexity of indoor environment, the level of automation and restrictions of input data. To address these issues, an automatic indoor reconstruction method that quickly and effectively reconstructs indoor environment of multi-floors and multi-rooms using both point clouds and trajectories from mobile laser scanning (MLS) is proposed. The proposed automatic method of parametric structure modeling comprises three steps. First, structural elements, such as doors, windows, walls, floors, and ceilings, are extracted based on the geometric and semantic features of point clouds. Then, the point clouds are automatically segmented into adjoining rooms through a combination of visibility analysis and physical constraints of the structural elements, which ensures the integrity of the room-space partitions and yields priors for the definition of point cloud label for reconstructed model. Finally, 3-D models of individual rooms are constructed by solving an energy optimization function via multi-label graph cuts. Three benchmark datasets collected by two handheld laser scanning (HLS) and a backpack laser scanning (BLS) system were used to evaluate the proposed method. Experiments demonstrate that the recall and precision of reconstructed surface models obtained by the proposed method are mostly larger than 60%, and the average F1-score of the model is close to 5 cm.

Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a spatial hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance.