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Gamification techniques have been proven effective in various fields such as education and industry. In this paper, we introduce a novel approach that applies gamification techniques to the identification of road assets in Mobile Laser Scanning (MLS) data. Our method utilises three gamification techniques: avatar (vehicle), point cloud segmentation into levels, and scoring. We implemented these techniques in Unreal Engine and evaluated their performance using three real-world case studies. We also compared two ways of point cloud visualisation: mesh-based and point-based. Our results demonstrate that our gamification approach improves the handling and visualisation of point clouds when compared to other free software such as Cloud Compare. Specifically, the point-based visualisation method provides a more accurate representation of the road environment and the input point cloud and is easier to import into Unreal Engine. However, this method requires more computational resources for visualisation. On the other hand, level segmentation ensures a constant frame rate of 60 frames per second. Furthermore, our gamification approach enhances the experience of road asset identification, making it more enjoyable for the user. However, we acknowledge that the quality of the point cloud remains the primary factor affecting the accuracy of asset identification, regardless of the software used. Overall, our proposed gamification approach offers a promising solution for improving the identification of road assets in MLS data and has the potential to be applied to other fields beyond road asset identification. [ABSTRACT FROM AUTHOR]

Applicability of SLAM (simultaneous localization and mapping) technology for mine surveys and subsequent 3D modelling of post-extracted surfaces is assessed. The resulting surface geometry is validated via terrestrial laser scanner (TLS) acquired reference data. Typical discrepancies remained within 2 and 5 cm in horizontal and vertical directions, respectively. Discrepancies between TLS, SLAM-enabled handheld scanner and conventional surveying results are small and fully satisfy the contemporary accuracy requirements, yet evidence that the conventional mine survey results are affected by the subjectivity of the surveyors. The SLAM-enabled laser scanning hence appears to be the most suitable method for underground mining surveys. [ABSTRACT FROM AUTHOR]

In most Mobile Laser Scanning (MLS) applications, filtering is a necessary step. In this paper, a segmentation-based filtering method is proposed for MLS point cloud, where a segment rather than an individual point is the basic processing unit. In particular, the MLS point clouds in some blocks are clustered into segments by a surface growing algorithm, and then the object segments are detected and removed. A segment-based filtering method is employed to detect the ground segments. The experiment in this paper uses two MLS point cloud datasets to evaluate the proposed method. Experiments indicate that, compared with the classic progressive TIN (Triangulated Irregular Network) densification algorithm, the proposed method is capable of reducing the omission error, the commission error and total error by 3.62%, 7.87% and 5.54% on average, respectively. [ABSTRACT FROM AUTHOR]

Detecting metro gauge is very important for the safe operation of the subway. In this study, we design a low-cost metro tunnel mobile scanning system (MDS-TJ-1), which integrates a laser profile scanner with an inertial measure unit and an odometer to provide positioning and attitude parameters of the trajectory. The Lagrange interpolation is used to accomplish the time unification of different sensors. A dynamic alignment scheme for profile scanner is proposed based on the designed plane reflector target with high reflectivity. The error accumulation of the odometer is corrected by recognising the tunnel longitudinal joints, and finish the multi-source data fusion. The horizontal ray method is developed to process the metro gauge inspection. The experiment results show that the alignment accuracy of scanner is within 8 mm, the inner coincidence of the point cloud is within 3 cm, and the average error of the gauge inspection is 7.8 mm. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) measurement technology is well-suited for individual-tree-based urban forest inventory because it can capture a dense three-dimensional (3D) point cloud from vertical structures of roadside trees in a rapid and cost-effective way. Street tree segmentation from MLS data is a preliminary and essential step for further extraction and analysis of tree attributes and is also a challenging problem because of the complex street environment. The accuracy of current methods is limited by information loss introduced by voxelization and artificially designed detection and segmentation rules based on prior knowledge that more or less lack ability to distinguish between street trees and other urban objects. To address the above issues, a two-step strategy for street tree segmentation method from MLS data was proposed, which worked on points directly instead of a set of voxels generated by spatial partition and made use of tree crown and trunk detectors trained by supervised learning algorithm. First, 16 local statistical features, including width, depth, elevation, dimensionality, and density features, were extracted from the sphere domain of each point using the grid index. Then these features were fused and tree crown and trunk detectors were trained from labelled MLS data of street through Discrete AdaBoost algorithm. Second, based on frame projection of point-wise prediction results of tree crown and trunk detectors, individual trees were located and segmented by exploiting non-connectivity between adjacent trees and adjacency within single trees. Experimental results have demonstrated that the proposed method can obtain point-wise street tree segmentation from MLS data with high accuracy tree crown and trunk detection without tedious detection or segmentation rules. The error rates of crown detector on test set were less than 0.3% accompanied by over 99.33% recall rates and over 98.59% precision rates and the error rates of trunk detector on test set were less than 0.06% accompanied by over 92.44% recall rates and over 94.14% precision rates. [ABSTRACT FROM AUTHOR]