Street-side vehicle detection, classification and change detection using mobile laser scanning data

Fast automatic three-dimensional road model reconstruction based on mobile laser scanning system

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

Abstract. The aim of the research is to evaluate the performance of the point cloud registration methods using mobile laser scanning data. The point cloud registration methods involved in this research are match bounding-box centres and iterative closest point (ICP). The research began with the two epoch’s mobile laser scanning survey using a Phoenix AL-3-32 system. At the same time, the stereo images of the study area were acquired using UAV Photogrammetric method. Both two epoch point cloud datasets were gone through the pre and post-processing stages to produce the cleaned and geo-referenced point clouds data. The data were then gone through the two registration methods and four Cloud-to-Cloud (C2C) distance methods. The 3D surface deviation results obtained from mobile laser scanning data was compared with the 3D surface deviation results from UAV data that undergoes the same registration and C2C distance computation methods. The study area involved in the research is an active landslide area that was located at Kulim Hi-Tech residential area in Kedah state, Malaysia. The study area exposed to the movement of the land which caused cracked to the buildings and drainages. The findings show that the ICP registration becomes the most suitable method to register point clouds dataset that was acquired using mobile laser scanning system. Among the four C2C distance computation methods that was involved in the testing, the least square plane method was the best method to calculate the distance between two sets of point clouds datasets which in turn gave the best results in the process of detecting the movement of the land in the study area.

Snow surface roughness from mobile laser scanning data

Adverse ground behavior events, such as convergence and ground falls, pose critical risks to underground mine safety and productivity. Today, monitoring of such failures is primarily conducted using legacy techniques with low spatial and temporal resolution while exposing workers to hazardous environments. This study assesses the potential of novel simultaneous localization and mapping (SLAM)-based light detection and ranging (Lidar) data quality for rapid, digital, and eventually autonomous mine-wide underground geotechnical monitoring. We derive a comprehensive suite of quality metrics based on tests in two underground mines for two state-of-the-art mobile laser scanning (MLS) systems. Our results provide evidence that SLAM-based MLS provides data of the quality required to detect geotechnically relevant changes while being significantly more efficient for large mine layouts when compared to traditional static systems. Additionally, we show that SLAM-specific processing can achieve an order of magnitude better relative accuracy relevant for change detection than quality metrics derived from traditionally deployed tests would suggest while reducing SLAM drift error by up to 90%. In collaboration with an operating block cave mine, we confirm these capabilities in field tests on a mine-wide scale and, for the first time, demonstrate methods of rockfall detection using MLS data. While more work is required to investigate optimal collection, processing, and utilization of MLS data, we demonstrate its potential to become an effective and widely applicable data source for rapid, accurate, and comprehensive geotechnical inspections.

The calibration and accuracy analysis of a novel, low-cost, adaptable mobile laser scanning (MLS) system using a Velodyne HDL-32E laser scanner and an Oxford Technical Solutions Inertial+2 inertial navigation system, is described. First, a static calibration of the laser scanner is discussed. The static calibration is shown to improve the overall relative accuracy of point cloud data from the scanner by approximately 20% over the manufacturer-supplied calibration. Then, the determination of system boresight angles and lever-arm offsets using a planar patch least-squares approach is presented. Finally, the calibrated and boresighted MLS is operated in a backpack mode to acquire multiple data sets in an area that contains dense ground control acquired using static terrestrial laser scanning (TLS) and a high-end, survey-grade MLS. The dense ground control is used to examine several methods of estimating the overall errors of the backpack MLS system. Detailed comparison of the MLS data with the TLS and survey-grade MLS control shows that, despite the system’s low cost, it is able to reliably collect point cloud data with greater than 10-cm three-dimensional root-mean-square error accuracy.

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

Mobile laser scanning (MLS) has emerged as a pivotal tool for accurately collecting tunnel point cloud data and enabling the detection of tunnel deformation. This study introduces a novel approach for the precise monitoring of tunnel cross-section deformation, a critical factor in assessing stability and lining safety. The MLS system used in this study is the Self-mobile Intelligent Laser Scanning System (SILSS) for data acquisition. A comparison with corresponding data acquired by Leica P16 demonstrates that the data collected by SILSS are accurate. The methodology developed utilizes ellipticity parameters and deformation analysis indices based on the ellipse-fitting analysis of circular shield tunnel deformation. A key innovation is the robust denoising of data using the Random Sample Consensus (RANSAC) method, ensuring accurate ellipse fitting and extraction of tunnel lining. Subsequently, an algorithm segmented the tunnel cross-section lining into individual shield tunnels, enabling the calculation of ellipticity parameters for shield tunnels, which are the objects for deformation analysis. The experimental results underscore the novelty and effectiveness of this approach in monitoring deformation across different indices. The method proves to be a reliable tool for assessing tunnel health, providing a detailed evaluation of the cross-section’s condition through statistical and graphical visualization. This study significantly advances shield tunnel monitoring, offering a practical and precise methodology for tunnel deformation analysis based on MLS point cloud data.

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value, but its development is severely hindered by the lack of suitable and specific datasets. Additionally, the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non‐overlapping special/rare categories, for example, rail track, track bed etc. To harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation, we introduce RailPC, a new point cloud benchmark. RailPC provides a large‐scale dataset with rich annotations for semantic segmentation in the railway environment. Notably, RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning (MLS) point cloud dataset and is the first railway‐specific 3D dataset for semantic segmentation. It covers a total of nearly 25 km railway in two different scenes (urban and mountain), with 3 billion points that are finely labelled as 16 most typical classes with respect to railway, and the data acquisition process is completed in China by MLS systems. Through extensive experimentation, we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation results. Based on our findings, we establish some critical challenges towards railway‐scale point cloud semantic segmentation. The dataset is available at https://github.com/NNU‐GISA/GISA‐RailPC, and we will continuously update it based on community feedback.

ABSTRACTIn this study, we evaluated the effects of topographic correction and gap filling of Landsat Enhanced Thematic Mapper Plus (ETM+) images on the accuracy of forest change detection through a trajectory-based approach. Four types of Landsat time series stacks (LTSS) were generated. These stacks resulted from combinations of topographically corrected and uncorrected imagery combined with gap-filled and unfilled stacks. These combinations of stacks were then used as input into a trajectory-based change detection. The results of change detection from trajectory-based analysis using these LTSS were compared in order to assess the effects of both topographic correction and gap-filling procedures on the ability to detect forest disturbances. The results showed that overall accuracies of change detection were improved after gap filling (10.5% and 7.5%), but were only slightly improved after topographic correction (3.6% and 0.6%). Although the gap-filling process introduced some uncertainty that might have caused false change detection, the number of pixels whose detection of disturbance was enhanced after gap filling exceeded those detecting false change. The results also showed that the topographic correction did not contribute much to improve the change detection in this study area. However, topographic correction has a potential to increase the accuracy of change detection in areas of more rugged terrain and steep slopes. This is because a direct relationship between the slope of the topography with topographic correction and an enhanced detection of disturbance in pixels from year to year was observed in this study. For robust change detection, we recommend that a gap-filling process should be included in the trajectory-based analysis procedures such as the one used in this study where a single image per year is used to characterize change. We also recommend that in areas of rugged terrain, a topographic correction in the image pre-processing should be implemented.

Abstract. Laser scanner intensity data provide valuable insights into material properties, enabling applications such as point cloud segmentation and material probing. However, extracting meaningful information is challenging due to the influence of the measurement configuration represented by the angle of incidence (AOI) and distance. Although existing methods for radiometric calibration in terrestrial laser scanning (TLS) exist, they rely on either overlapping scans from discrete positions or on manual segmentation. This limits their applicability to mobile laser scanning (MLS), which typically produces very large datasets (requiring automation) with little or no overlap, and from continuously changing positions. This study presents an approach for adapting an automatic in-situ radiometric calibration method originally developed for TLS that applies to MLS. Building on our previous work, we introduce techniques to estimate AOI and distance influence compensation functions with little or without overlap, as well as non-discrete scan stations, and propose two strategies for AOI influence compensation - global and local. The global method computes one best-fitting AOI compensation function for the entire scan. It uses local reflectance estimation, which relies on a modified filtering technique, accommodating the unique characteristics of MLS data. The local method computes the best-fitting AOI compensation function per segment, ideally containing a single material. We use machine learning for point cloud semantic segmentation with additional instance segmentation to automatically obtain a material proxy segment. We evaluate the proposed methods on four datasets captured by two different MLS systems, demonstrating their ability to reduce measurement configuration related influences on intensities and enhance following point cloud segmentation.

Abstract. The study deals with the automatic supervised classification of urban objects from point clouds collected by the vehicle-based Mobile Laser Scanning (MLS) system. A benchmark dataset representing the Technical University of Munich (TUM) City Campus was used. The main contribution of this article is evaluating the performance difference between kNN, cylindrical and spherical local neighborhood relations in point-based classification of an MLS system using local geometric and shape-based features. The Random Forest (RF) classifier was performed for 8 manually marked classes in the benchmark set: artificial terrain, natural terrain, high vegetation, low vegetation, building, hardscape, artifact and vehicle. We reveal that the cylindrical neighborhood with 13 attributes provides an improvement of 5.2% compared to the spherical neighborhood, while the kNN gave almost the same result as the cylindrical neighborhood (0.8% improvement) in the shortest time. Finally, a new feature set was created by combining the most important features obtained from different neighborhood types. As a result, we achieved 96.9% overall accuracy by using 19 significant features obtained from all neighborhood types for the TUM-MLS1 point cloud.

Review of mobile mapping and surveying technologies

Nowadays, 3D modelling has become very important because real information can be extracted from the model. UAV is system that was used for developing the 3D model and has proven capable to produce good results. However, the exploration of the integration of UAV with other technology like LiDAR and MLS to develop a more accurate and detailed model is still lacking. This study reviews the aptitudes of the UAV integrated with LiDAR and MLS for developing the 3D building model in Malaysia. Several issues, like type of platform, quality of 3D model and others to review the capabilities of the UAV used for producing very accurate 3D model were critically analysed. Previous research suggests that integration of Lidar with multi-rotor platform fusing with MLS could be used to construct an accurate 3D model. A dense point cloud of the whole building can be obtained from the fusion between Lidar and MLS. Moreover, an accurate 3D model can be generated from this process as the point-cloud from LiDAR and MLS has positional accuracy in centimetre level compared to a non-metric camera integrated with a UAV. This study finds the UAV has the potential to use to produce a quality 3D model.