Mobile Laser Scanning Systems Research Articles

SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR

Accurately retrieving tree stem location distributions is a basic requirement for biomass estimation of forest inventory. Combining Inertial Measurement Units (IMU) with Global Navigation Satellite Systems (GNSS) is a commonly used positioning strategy in most Mobile Laser Scanning (MLS) systems for accurate forest mapping. Coupled with a tactical or consumer grade IMU, GNSS offers a satisfactory solution in open forest environments, for which positioning accuracy better than one decimeter can be achieved. However, for such MLS systems, positioning in a mature and dense forest is still a challenging task because of the loss of GNSS signals attenuated by thick canopy. Most often laser scanning sensors in MLS systems are used for mapping and modelling rather than positioning. In this paper, we investigate a Simultaneous Localization and Mapping (SLAM)-aided positioning solution with point clouds collected by a small-footprint LiDAR. Based on the field test data, we evaluate the potential of SLAM positioning and mapping in forest inventories. The results show that the positioning accuracy in the selected test field is improved by 38% compared to that of a traditional tactical grade IMU + GNSS positioning system in a mature forest environment and, as a result, we are able to produce a unambiguous tree distribution map.

Automated extraction of ground surface along urban roads from mobile laser scanning point clouds

ABSTRACTExtracting ground surface from high-density point clouds collected by Mobile Laser Scanning (MLS) systems is of vital importance in urban planning and digital city mapping. This article proposes a novel approach for automated extraction of ground surface along urban roads from MLS point clouds. The approach, which was designed to handle both ordered and unordered MLS point clouds, consists of three key steps: constructing vertical profile from MLS point clouds along the vehicle trajectory; extracting candidate ground points using an adaptive alpha shapes algorithm; refining the candidate ground points with an elevation variance filter. To evaluate the performance of the proposed method, experiments were conducted using two types of urban street-scene point clouds. The results reveal that the ground points can be detected with an error rate of as low as 1.9%, proving that our proposed method offers a promising solution for automated extraction of ground surface from MLS point clouds.

PARAMETERIC ANALYSIS FOR AUTOMATED EXTRACTION OF ROAD EDGES FROM MOBILE LASER SCANNING DATA

Abstract. The applicability of Mobile Laser Scanning (MLS) systems continue to prove their worth in route corridor mapping due to the rapid, continuous and cost effective 3D data acquisition capability. LiDAR data provides a number of attributes which can be useful for extracting various road features. Road edge is a fundamental feature and its accurate knowledge increases the reliability and precision of extracting other road features. We developed an automated algorithm for extracting left and right edges from MLS data. The algorithm involved several input parameters which are required to be analysed in order to find their optimal values. In this paper, we present a detailed analysis of the dimension parameters of input data and raster cell in our algorithm. These parameters were analysed based on temporal, completeness and accuracy performance of our algorithm for their different sets of values. This analysis provided the estimation of an optimal values of parameters which were used to automate the process of extracting road edges from MLS data.

Street environment change detection from mobile laser scanning point clouds

Mobile laser scanning (MLS) has become a popular technique for road inventory, building modelling, infrastructure management, mobility assessment, etc. Meanwhile, due to the high mobility of MLS systems, it is easy to revisit interested areas. However, change detection using MLS data of street environment has seldom been studied. In this paper, an approach that combines occupancy grids and a distance-based method for change detection from MLS point clouds is proposed. Unlike conventional occupancy grids, our occupancy-based method models space based on scanning rays and local point distributions in 3D without voxelization. A local cylindrical reference frame is presented for the interpolation of occupancy between rays according to the scanning geometry. The Dempster–Shafer theory (DST) is utilized for both intra-data evidence fusion and inter-data consistency assessment. Occupancy of reference point cloud is fused at the location of target points and then the consistency is evaluated directly on the points. A point-to-triangle (PTT) distance-based method is combined to improve the occupancy-based method. Because it is robust to penetrable objects, e.g. vegetation, which cause self-conflicts when modelling occupancy. The combined method tackles irregular point density and occlusion problems, also eliminates false detections on penetrable objects.

Automatic Extraction of Railroad Centerlines from Mobile Laser Scanning Data

In this paper, we describe the automatic extraction of centerlines of railroads. Mobile Laser Scanning systems are able to capture the 3D environment of the rail tracks with a high level of detail. Our approach first detects laser points that were reflected by the rail tracks, by making use of local properties such as parallelism and height in relation to neighboring objects. In the modeling stage, we present two approaches to determine the centerline location. The first approach generates center points in a data-driven manner by projecting rail track points to the parallel track, and taking the midpoint as initial center point. Next, a piecewise linear function is fitted through the center points to generate center points at a regular interval. The second approach models the rail track by fitting piecewise 3D track models to the rail track points. The model consists of a pair of two parallel rail tracks. The fitted pieces are smoothened by a Fourier series interpolation function. After that the centerline is implicitly determined by the geometric center of the pair of tracks. Reference data has been used to analyze the quality of our results, confirming that the position of the centerlines can be determined with an accuracy of 2–3 cm.

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

In the Traffic, Urban planning, and Construction domain, rich three-dimensional information models are throughout a whole life cycle for road application, such as road planning, road development, road operation, road reuse, and road renewal. These three-dimensional models are often constructed using dense three-dimensional point clouds obtained from laser scanning systems. The latest mobile terrestrial laser scanning systems provide an efficient technique for acquiring very dense point clouds along road corridors, so that fast and automated procedures for recognizing or extracting road attributes and reconstructing road three-dimensional models can be developed. In this paper, every major step of the whole road model reconstruction process, from the input point clouds to the finished model, is presented in detail. The results show that the mobile laser scanning system allows an efficient and automatic reconstruction for urban road three-dimensional model.

An Algorithm for Automated Estimation of Road Roughness from Mobile Laser Scanning Data

AbstractRoad roughness is the deviation of a road surface from a designed surface grade that influences safety conditions for road users. Mobile laser scanning (MLS) systems provide a rapid, continuous and cost‐effective way of collecting highly accurate and dense 3D point‐cloud data along a route corridor. In this paper an algorithm for the automated estimation of road roughness from MLS data is presented, where a surface grid is fitted to the lidar points associated with the road surface. The elevation difference between the lidar points and their surface grid equivalents provides residual values in height which can be used to estimate roughness along the road surface. Tests validated the new road‐roughness algorithm by successfully estimating surface conditions on multiple road sections. These findings contribute to a more comprehensive approach to surveying road networks.

Conceptual Issues Regarding the Development of Underground Railway Laser Scanning Systems

Mobile Laser Scanning (MLS) systems are widely applied for spatial data collection and support applications in many aspects. In recent years, MLS technology had been introduced to railway applications and greatly enhanced the spatial detail and efficiency when compared to traditional approaches. However, the advance of MLS technology is not completely applied to railway environment. Typical MLS systems rely on integrated navigation through the use of Inertial Navigation Systems (INS) and Global Navigation Satellite Systems (GNSS) for geo-referencing, while operation under long-term GNSS outages or even GNSS-free environments, such as underground railway or long tunnels, remains a challenging issue due to the degraded operation of standalone inertial navigation. Commercial MLS systems usually employ high performance inertial measurement units (IMU) and various strategies to manage GNSS outages, but GNSS components are still necessary prior to and after experiencing the loss of GNSS signals. To tackle the problem of permanent GNSS outages, alternative methods are introduced to replace the GNSS and so allow the use of MLS systems in GNSS-free underground railway environments. Such approaches encourage the MLS systems to be developed into the Underground Railway Laser Scanning (URLS) systems, which may provide several alternative operational functions for the management of underground railway operation.

Development, Calibration and Evaluation of a Portable and Direct Georeferenced Laser Scanning System for Kinematic 3D Mapping

AbstractIn recent years, kinematic laser scanning has become increasingly popular because it offers many benefits compared to static laser scanning. The advantages include both saving of time in the georeferencing and a more favorable scanning geometry. Often mobile laser scanning systems are installed on wheeled platforms, which may not reach all parts of the object. Hence, there is an interest in the development of portable systems, which remain operational even in inaccessible areas. The development of such a portable laser scanning system is presented in this paper. It consists of a lightweight direct georeferencing unit for the position and attitude determination and a small low-cost 2D laser scanner. This setup provides advantages over existing portable systems that employ heavy and expensive 3D laser scanners in a profiling mode.A special emphasis is placed on the system calibration, i. e. the determination of the transformation between the coordinate frames of the direct georeferencing unit and the 2D laser scanner. To this end, a calibration field is used, which consists of differently orientated georeferenced planar surfaces, leading to estimates for the lever arms and boresight angles with an accuracy of mm and one-tenth of a degree. Finally, point clouds of the mobile laser scanning system are compared with georeferenced point clouds of a high-precision 3D laser scanner. Accordingly, the accuracy of the system is in the order of cm to dm. This is in good agreement with the expected accuracy, which has been derived from the error propagation of previously estimated variance components.

Object Detection in Terrestrial Laser Scanning Point Clouds Based on Hough Forest

This letter presents a novel rotation-invariant method for object detection from terrestrial 3-D laser scanning point clouds acquired in complex urban environments. We utilize the Implicit Shape Model to describe object categories, and extend the Hough Forest framework for object detection in 3-D point clouds. A 3-D local patch is described by structure and reflectance features and then mapped to the probabilistic vote about the possible location of the object center. Objects are detected at the peak points in the 3-D Hough voting space. To deal with the arbitrary azimuths of objects in real world, circular voting strategy is introduced by rotating the offset vector. To deal with the interference of adjacent objects, distance weighted voting is proposed. Large-scale real-world point cloud data collected by terrestrial mobile laser scanning systems are used to evaluate the performance. Experimental results demonstrate that the proposed method outperforms the state-of-the-art 3-D object detection methods.

Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems

Abstract. The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface reconstruction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.

Automated road markings extraction from mobile laser scanning data

Road markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.

An automated algorithm for extracting road edges from terrestrial mobile LiDAR data

Terrestrial mobile laser scanning systems provide rapid and cost effective 3D point cloud data which can be used for extracting features such as the road edge along a route corridor. This information can assist road authorities in carrying out safety risk assessment studies along road networks. The knowledge of the road edge is also a prerequisite for the automatic estimation of most other road features. In this paper, we present an algorithm which has been developed for extracting left and right road edges from terrestrial mobile LiDAR data. The algorithm is based on a novel combination of two modified versions of the parametric active contour or snake model. The parameters involved in the algorithm are selected empirically and are fixed for all the road sections. We have developed a novel way of initialising the snake model based on the navigation information obtained from the mobile mapping vehicle. We tested our algorithm on different types of road sections representing rural, urban and national primary road sections. The successful extraction of road edges from these multiple road section environments validates our algorithm. These findings and knowledge provide valuable insights as well as a prototype road edge extraction tool-set, for both national road authorities and survey companies.

Performance Analysis of Mobile Laser Scanning Systems in Target Representation

The technology of mobile laser scanning (MLS) has developed rapidly in recent years. This speedy development is evidenced by the emergence of a variety of MLS systems in commercial market and academic institutions. However, the producers tend to supply the specifications of the individual sensors in a generic sense, and this is not enough for guiding the choice of a MLS system for a specific application case. So far, the research efforts comparing the efficacy ranges of the existing MLS systems have been little reported. To fill this gap, this study examined the performance of three typical MLS systems (Riegl VMX-250, Roamer and Sensei) in terms of target representation. Retrievals of window areas and lighting pole radiuses served as representative cases, as these parameters correspond to the spatial scales from meter to centimeter. The evaluations showed that the VMX-250 with highest sampling density did best, and thus, it was preferred in the scenario of this study. If both the cost and efficacy were regarded, Roamer was a choice of compromise. Therefore, an application-oriented scheme was suggested for selecting MLS systems to acquire the desired performance.

Derivation of tree skeletons and error assessment using LiDAR point cloud data of varying quality

The architecture of trees is of particular interest for 3D model creation in forestry and ecolocical applications. Terrestrial (TLS) and mobile laser scanning (MLS) systems are used to acquire detailed geometrical data of trees. Since 3D point clouds from laser scanning consist of large data amounts representing uninterpreted topographical information including noise and data gaps, an extraction of salient tree structures is important for further applications. We present a fully automated modular workflow for topological reliable reconstruction of tree architecture. Object-based point cloud processing such as branch extraction is combined with tree skeletonization. Branch extraction is performed using a segmentation procedure followed by segment-based analysis of form indices derived from eigenvector metrics. Extracted branch primitives are simplified and connected to line features during skeletonization. The modular workflow allows comprehensive parameter tests and error assessments that are used for a calibration of the module parameters with respect to various characteristics of the input data (e.g noise, scanning resolution, and the number of scan positions). The estimated parameter settings are validated using an exemplary MLS data set. The quality of input point cloud data, strongly influencing the quality of the skeleton results, can be improved by the presented branch extraction procedure. The potential for data improvement increases with increasing point densities. For our object-based appoach, we can show that the presence of erroneous structures and filtering artifacts have the strongest influence onto the quality of the derived skeletons. In contrast to traditional skeletonization approaches, the existance of data gaps has less influence onto the results.

Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field

The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project “Mobile Mapping—Road Environment Mapping Using Mobile Laser Scanning”. Several commercial and research systems collected laser point cloud data on the same test field. The system comparisons focused on planimetric and elevation errors using a filtered digital elevation model, poles, and building corners as the reference objects. The results revealed the high quality of the point clouds generated by all of the tested systems under good GNSS conditions. With all professional systems properly calibrated, the elevation accuracy was better than 3.5 cm up to a range of 35 m. The best system achieved a planimetric accuracy of 2.5 cm over a range of 45 m. The planimetric errors increased as a function of range, but moderately so if the system was properly calibrated. The main focus on mobile laser scanning development in the near future should be on the improvement of the trajectory solution, especially under non-ideal conditions, using both improvements in hardware and software. Test fields are relatively easy to implement in built environments and they are feasible for verifying and comparing the performance of different systems and also for improving system calibration to achieve optimum quality.

LIDAR AND INS FUSION IN PERIODS OF GPS OUTAGES FOR MOBILE LASER SCANNING MAPPING SYSTEMS

Abstract. Mobile laser scanning systems are becoming an increasingly popular means to obtain 3D coverage on a large scale. To perform the mapping, the exact position of the vehicle must be known throughout the trajectory. Exact position is achieved via integration of Global Positioning Systems (GPS) and Inertial Navigation Systems (INS). Yet, in urban environments, cases of complete or even partial GPS outages may occur leaving the navigation solution to rely only on the INS. The INS navigation solution degrades with time as the Inertial Measurement Unit (IMU) measurements contains noise, which permeates into the navigation equations. Degradation of the position determination leads to loss of data in such segments. To circumvent such drift and its effects, we propose fusing INS with lidar data by using building edges. This detection of edges is then translated into position data, which is used as an aiding to the INS. It thereby enables the determination of the vehicle position with a satisfactory level accuracy, sufficient to perform the laser-scanning based mapping in those outage periods.

A LOW BUDGET MOBILE LASER SCANNING SOLUTION USING ON BOARD SENSORS AND FIELD BUS SYSTEMS OF TODAY'S CONSUMER AUTOMOBILES

Abstract. Mobile laser scanning systems (MLS) offer a great potential for acquiring detailed point cloud data of urban and suburban surroundings with minimum effort. In this paper a new solution for MLSs is presented, requiring solely a combination of a profile laser scanning device and systems that are included in today's serialized end consumer vehicles. While today's mobile laser scan systems require different and expensive additional hardware that needs to be mounted onto the vehicle, the devices included within vehicle electronics offer good alternatives without additional costs.The actual scan consists of a continuous profile scan together with information gathered from on-board sensor modules. In a post- processing step, the sensor data is used to reconstruct the car's trajectory for the period of the scan and, based on this information, the track of the scan device for every measured laser pixel. Synchronization of pixel data and vehicle movement is realized via a timestamp signal which is transmitted to the car's field bus system and the scan device. To generate the final point cloud scenario, the trajectory is interpolated for every single scan point and used to convert its local position within the profile into the global coordinate system (Fig.1, Left).

LAND-BASED MOBILE LASER SCANNING SYSTEMS: A REVIEW

Abstract. Mobile mapping has been using various photogrammetric techniques for many years. In recent years, there has been an increase in the number of mobile mapping systems using laser scanners available in the market, partially because of the improvement in GNSS/INS performance for direct georeferencing. In this article, some of the most important land-based mobile laser scanning (MLS) systems are reviewed. Firstly, the main characteristics of MLS systems vs. airborne (ALS) and terrestrial laser scanning (TLS) systems are compared. Secondly, a short overview of the mobile mapping technology is also provided so that the reader can fully grasp the complexity and operation of these devices. As we put forward in this paper, a comparison of different systems is briefly carried out regarding specifications provided by the manufacturers. Focuses on the current research are also addressed with emphasis on the practical applications of these systems. Most of them have been utilized for data collection on road infrastructures or building façades. This article shows that MLS technology is nowadays well established and proven, since the demand has grown to the point that there are several systems suppliers offering their products to satisfy this particular market.

BENCHMARKING MOBILE LASER SCANNING SYSTEMS USING A PERMANENT TEST FIELD

Abstract. The objective of the study was to benchmark the geometric accuracy of mobile laser scanning (MLS) systems using a permanent test field under good coverage of GNSS. Mobile laser scanning, also called mobile terrestrial laser scanning, is currently a rapidly developing area in laser scanning where laser scanners, GNSS and IMU are mounted onboard a moving vehicle. MLS can be considered to fill the gap between airborne and terrestrial laser scanning. Data provided by MLS systems can be characterized with the following technical parameters: a) point density in the range of 100-1000 points per m2 at 10 m distance, b) distance measurement accuracy of 2-5 cm, and c) operational scanning range from 1 to 100 m. Several commercial, including e.g. Riegl, Optech and others, and some research mobile laser scanning systems surveyed the test field using predefined driving speed and directions. The acquired georeferenced point clouds were delivered for analyzing. The geometric accuracy of the point clouds was determined using the reference targets that could be identified and measured from the point cloud. Results show that in good GNSS conditions most systems can reach an accuracy of 2 cm both in plane and elevation. The accuracy of a low cost system, the price of which is less than tenth of the other systems, seems to be within a few centimetres at least in ground elevation determination. Inaccuracies in the relative orientation of the instruments lead to systematic errors and when several scanners are used, in multiple reproductions of the objects. Mobile laser scanning systems can collect high density point cloud data with high accuracy. A permanent test field suits well for verifying and comparing the performance of different mobile laser scanning systems. The accuracy of the relative orientation between the mapping instruments needs more attention. For example, if the object is seen double in the point cloud due to imperfect boresight calibration between two scanners, this will make especially the automatic modelling of the object much more challenging.