Mobile Laser Scanning Systems Research Articles

Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

Efficient point cloud corrections for mobile monitoring applications using road/rail-side infrastructure

Light Detection and Ranging (LiDAR) systems are known to capture high density and accuracy data much more efficiently than other surveying methods. Therefore they are used for many applications, e.g. mobile mapping and surveying, 3D modelling, hazard detection, etc. However, while the accuracy of the laser measurements is very high, the accuracy of the resulting 3D point cloud is greatly affected by the geo-referencing accuracy. This is especially problematic for mobile laser scanning systems, where the LiDAR is installed on a moving platform, e.g. a vehicle, and the point cloud is geo-referenced by the data provided by a navigation system. Owing to the complexity of the surrounding environments and external conditions, the accuracy of the navigation system varies and thereby changes the quality of the point cloud. Conventional methods for correcting the point cloud accuracy either rely heavily on manual work or semi-automatic registration methods. While they can provide geo-referencing under different conditions, each has their own problems. This paper presents a semi-automated geo-referencing trajectory correction method by extracting features from the pre-processed point cloud and integrating this information to reprocess the navigation trajectory which is then able to produce better quality point clouds. The method deals with the changing errors within a point cloud dataset, and reducing the trajectory error from metre level to decimetre level, improving the accuracy by at least 56%. The accuracy of the regenerated point cloud then becomes suitable for many accuracy-demanding monitoring and change detection applications.

Automated Method of Extracting Urban Roads Based on Region Growing from Mobile Laser Scanning Data.

With the rapid development of three-dimensional point cloud acquisition from mobile laser scanning systems, the extraction of urban roads has become a major research focus. Although it has great potential for digital image processing, the extraction of roads using the region growing approach is still in its infancy. We propose an automated method of urban road extraction based on region growing. First, an initial seed is chosen under constraints relating to the Gaussian curvature, height and number of neighboring points, which ensures that the initial seed is located on a road. Then, the growing condition is determined by the angle threshold of the tangent plane of the seed point. Then, new seeds are selected based on the identified road points and their curvature. The method also includes a strategy for dealing with multiple discontinuous roads in a dataset. The result shows that the method can not only achieve high accuracy in urban road extraction but is also stable and robust.

Automated Inspection of Railway Tunnels’ Power Line Using LiDAR Point Clouds

Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser scanning (MLS) systems use a laser beam to collect dense three dimensional (3D) point clouds, which include geometric and radiometric data of the environment in which they are placed. In the context of this paper, a methodology for automatically inspecting the clearance gauge and the deflection of the aerial contact line in railway tunnels is presented. The main objective is to compare results and verify their compliance with the Spanish norm. The 3D data are provided by a LYNX Mobile Mapper System (MMS). First, the area is surveyed and then the obtained (3D) point cloud is classified into contact wire, suspension wire, and remaining points. Finally, the inspection of the railway’s power line is performed. The validation of the proposed methodology has been carried out in three different tunnel point clouds, obtaining both qualitative and quantitative results for points’ classification, together with the results of the measures performed.

A breakline-preserving ground interpolation method for MLS data

ABSTRACTModelling accurate ground surfaces in urban areas is important for surveying and mapping. Breaklines along roads are critical for both digital elevation models and high precision maps. This article presents a new breakline-preserving ground interpolation method for point clouds acquired by mobile laser scanning (MLS). The proposed method needs only point coordinates as input. It first initializes unknown regions by the nearest neighbouring interpolation then matched patches in known regions are found along edges. Next, Poisson interpolation is utilized to improve the elevation accuracy. An edge-guided patch regularization method is applied before patch blending to remove noise and improve gradient accuracy. The proposed method was tested on two datasets acquired by different MLS systems. This paper quantitatively evaluates the proposed method as well as the existing solution in two samples of complete points. These results demonstrate that the proposed method outperforms the existing method in terms of visual coherence and mean absolute differences. Moreover, the edge information which is self-contained in point clouds has been proven useful in ground interpolation.

A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas

Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.

Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

AUTOMATED VISIBILITY FIELD EVALUATION OF TRAFFIC SIGN BASED ON 3D LIDAR POINT CLOUDS

Abstract. Maintaining high visibility of traffic signs is very important for traffic safety. Manual inspection and removal of occlusion in front of traffic signs is one of the daily tasks of the traffic management department. This paper presents a method that can automatically detect the occlusion and continuously quantitative estimate the visibility of traffic sign cover all the road surface based on Mobile Laser Scanning (MLS) systems. The concept of traffic sign’s visibility field is proposed in this paper. One of important innovation of this paper is that we use retinal imaging area to evaluate the visibility of a traffic sign. And this makes our method is in line with human vision. To validate the reasonable and accuracy of our method, we use the 2D and 3D registration technology to observe the consistence of the occlusion ratio in point clouds with it in photo. Experiment of implementation on large scale traffic environments show that our method is feasible and efficient.

Point clouds for direct pedestrian pathfinding in urban environments

Pathfinding applications for the citizen in urban environments are usually designed from the perspective of a driver, not being effective for pedestrians. In addition, urban scenes have multiple elements that interfere with pedestrian routes and navigable space. In this paper, a methodology for the direct use of point clouds for pathfinding in urban environments is presented, solving the main limitations for this purpose: (a) the excessive number of points is reduced for transformation into nodes on the final graph, (b) urban static elements acting as permanent obstacles, such as furniture and trees, are delimited and differentiated from dynamic elements such as pedestrians, (c) occlusions on ground elements are corrected to enable a complete graph modelling, and (d) navigable space is delimited from free unobstructed space according to two motor skills (pedestrians without reduced mobility and wheelchairs). The methodology is tested into three different streets sampled as point clouds by mobile laser scanning (MLS) systems: an intersection of several streets with ground composed of sidewalks at different heights; an avenue with wide sidewalks, trees and cars parked on one side; and a street with a single-lane road and narrow sidewalks. By applying Dijkstra pathfinding algorithm to the resulting graphs, the correct viability of the generated routes has been verified based on a visual analysis of the generated routes on the point cloud and on the knowledge of the urban study area. The methodology enables the automatic generation of graphs representing the navigable urban space, on which safe and real routes for different motor skills can be calculated.

Efficient and robust lane marking extraction from mobile lidar point clouds

Surveys of roadways with Mobile Laser Scanning (MLS) are now being conducted on a regular basis by many transportation agencies to provide detailed geometric information to support a wide range of applications, including asset management. Most MLS systems provide intensity (return signal strength) data as a point attribute in georeferenced point clouds, which may be used to estimate retro-reflectivity of pavement markings for effective maintenance. Nevertheless, the extraction of pavement markings from mobile lidar data remains an open challenge, due to variable noise, degree of wear on the markings, and road conditions. This paper addresses these challenges, presenting a novel approach for efficient, reliable extraction of lane markings, including those that have been significantly worn. First, using the MLS trajectory information, the lidar data is discretized into smaller sections, and then transformed to the local coordinate system, such that the road surface is near-horizontal for reliable extraction on roads with significant grade. Subsequently, the road surface is extracted using the constrained Random Sampling and Consensus (RANSAC) algorithm and then rasterized into a 2D intensity image to apply image processing techniques, namely: image segmentation to separate the lane markings from the road pavement, and a morphological opening operation to remove small objects. However, the extracted lane markings are prone to over-segmentation, due to occlusions or worn portions caused by moving vehicles. To rectify this, topologically-similar lane markings are associated with each other by computing line parameters (i.e., orientation and distance from the origin), which enables the gaps to be filled among the associated lanes. Finally, the remaining incorrect lane markings are detected and removed through a noise filtering phase using Dip test statistics. Examples of the effectiveness and application of the methodology are shown for a variety of sites with stripes of variable condition to highlight the robustness of the approach. Using optimized parameter values, the algorithm achieved F1 scores of 89–97% when tested on a variety of datasets encompassing a wide range of road scene types.

KINEMATIC CALIBRATION USING LOW-COST LiDAR SYSTEM FOR MAPPING AND AUTONOMOUS DRIVING APPLICATIONS

Abstract. More recently, mapping sensors for land-based Mobile Mapping Systems (MMSs) have combined cameras and laser scanning measurements defined as Light Detection and Ranging (LiDAR), or laser scanner together. These mobile laser scanning systems (MLS) can be used in dynamic environments and are able of being adopted in traffic-related applications, such as the collection of road network databases, inventory of traffic sign and surface conditions, etc. However, most LiDAR systems are expensive and not easy to access. Moreover, due to the increasing demand of the autonomous driving system, the low-cost LiDAR systems, such as Velodyne or SICK, have become more and more popular these days. These kinds of systems do not provide the total solution. Users need to integrate with Inertial Navigation System/ Global Navigation Satellite System (INS/GNSS) or camera by themselves to meet their requirement. The transformation between LiDAR and INS frames must be carefully computed ahead of conducting direct geo-referencing. To solve these issues, this research proposes the kinematic calibration model for a land-based INS/GNSS/LiDAR system. The calibration model is derived from the direct geo-referencing model and based on the conditioning of target points where lie on planar surfaces. The calibration parameters include the boresight and lever arm as well as the plane coefficients. The proposed calibration model takes into account the plane coefficients, laser and INS/GNSS observations, and boresight and lever arm. The fundamental idea is the constraint where geo-referenced point clouds should lie on the same plane through different directions during the calibration. After the calibration process, there are two evaluations using the calibration parameters to enhance the performance of proposed applications. The first evaluation focuses on the direct geo-referencing. We compared the target planes composed of geo- referenced points before and after the calibration. The second evaluation concentrates on positioning improvement after taking aiding measurements from LiDAR- Simultaneously Localization and Mapping (SLAM) into INS/GNSS. It is worth mentioning that only one or two planes need to be adopted during the calibration process and there is no extra arrangement to set up the calibration field. The only requirement for calibration is the open sky area with the clear plane construction, such as wall or building. Not only has the contribution in MMSs or mapping, this research also considers the self-driving applications which improves the positioning ability and stability.

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

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.

Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories

Terrestrial laser scanning (TLS) has been successfully used for three-dimensional (3D) data capture in forests for almost two decades. Beyond the plot-based data capturing capabilities of TLS, vehicle-based mobile laser scanning (MLS) systems have the clear advantage of fast and precise corridor-like 3D data capture, thus providing a much larger coverage within shorter acquisition time. This paper compares and discusses advantages and disadvantages of multi-temporal MLS data acquisition compared to established TLS data recording schemes. In this pilot study on integrated TLS and MLS data processing in a forest, it could be shown that existing TLS data evaluation routines can be used for MLS data processing. Methods of automatic laser scanner data processing for forest inventory parameter determination and quantitative structure model (QSM) generation were tested in two sample plots using data from both scanning methods and from different seasons. TLS in a multi-scan configuration delivers very high-density 3D point clouds, which form a valuable basis for generating high-quality QSMs. The pilot study shows that MLS is able to provide high-quality data for an equivalent determination of relevant forest inventory parameters compared to TLS. Parameters such as tree position, diameter at breast height (DBH) or tree height can be determined from MLS data with an accuracy similar to the accuracy of the parameter derived from TLS data. Results for instance in DBH determination by cylinder fitting yielded a standard deviation of 1.1 cm for trees in TLS data and 3.7 cm in MLS data. However, the resolution of MLS scans was found insufficient for successful QSM generation. The registration of MLS data in forests furthermore requires additional effort in considering effects caused by poor GNSS signal.

AUTOMATIC CLASSIFICATION OF POINT CLOUDS FOR HIGHWAY DOCUMENTATION

Mobile laser scanning systems confirmed the capability for detailed roadway documentation. Hand in hand with enormous datasets acquired by these systems is the increase in the demands on the fast and effective processing of these datasets. The crucial part of the roadway datasets processing, as well as in many other applications, is the extraction of objects of interest from point clouds. In this work, an approach to the rough classification of mobile laser scanning data based on raster image processing techniques is presented. The developed method offers a solution for a computationally low demanding classification of the highway environment. The aim of this method is to provide a background for the easier use of more sophisticated algorithms and a specific analysis. The method is evaluated using different metrics on a 1.8km long dataset obtained by LYNX Mobile Mapper over a highway.

Highway Cross-Slope Measurement using Mobile LiDAR

Ensuring adequate pavement cross-slope on highways can improve driver safety by reducing the potential for ponding to occur or vehicles to hydroplane. Mobile laser scanning (MLS) systems provide a rapid, continuous, and cost-effective means of collecting accurate 3D coordinate data along a corridor in the form of a point cloud. This study provides an evaluation of MLS systems in terms of the accuracy and precision of collected cross-slope data and documentation of procedures needed to calibrate, collect, and process this data. Mobile light detection and ranging (LiDAR) data were collected by five different vendors on three roadway sections. The results indicate the difference between ground control adjusted and unadjusted LiDAR derived cross-slopes, and field surveying measurements less than 0.19% at a 95% confidence level. The unadjusted LiDAR data incorporated corrections from an integrated inertial measurement unit and high-accuracy real-time kinematic GPS, however it was not post-processed adjusted with ground control points. This level of accuracy meets suggested cross-slope accuracies for mobile measurements (±0.2%) and demonstrates that mobile LiDAR is a reliable method for cross-slope verification. Performing cross-slope verification can ensure existing pavement meets minimum cross-slope requirements, and conversely is useful in identifying roadway sections that do not meet minimum standards, which is more desirable than through crash reconnaissance where hydroplaning was evident. Adoption of MLS would enable the South Carolina Department of Transportation (SCDOT) to address cross-slope issues through efficient and accurate data collection methods.

Automatic non-rigid registration of multi-strip point clouds from mobile laser scanning systems

ABSTRACTMobile laser scanning (MLS) systems equipped with precise Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) positioning devices are being used at an increasing rate for production of the high-accurate driving maps because of its safety and high performance in collection of 3D spatial data. In practice, GNSS signals may be blocked out by trees or buildings etc., and the errors of IMU are accumulated over time, leading to misalignments ranging from decimetre level to sub-metre level between point clouds from back and forth scans, or among multiple excursions. In this article, we propose a new time-variant model and an automatic solution to align the multi-strip MLS point clouds. Our methods are divided into three key steps: preprocessing to get representative points, two-step Iterative Closest Point registration to obtain correspondences, and time-variant errors estimation and correction of point clouds. We verified the solution using test data scanned in city road and highway environment. The experimental results demonstrate that the precision of the point clouds is significantly improved and the root mean square errors are about 4–5 cm.

Extended investigation into continuous laser scanning of underground mine workings by means of Landis inertial navigation system

The paper investigates the method of applying mobile scanning systems (MSSs) with inertial navigators in the underground conditions for carrying out the surveying tasks. The available mobile laser scanning systems cannot be used in the underground environment since Global Positioning System (GPS) signals cannot be received in mines. This signal not only is necessary for space positioning, but also operates as the main corrective signal for the primary navigation system – the inertial navigation system. The idea of the method described in this paper consists in using MSSs with a different correction of the inertial system than GPS is.

CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS FROM HEIGHT FEATURES

Abstract. The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features – two height components and one reflectance value, and achieved an overall accuracy of 73 %, which is really encouraging for further refining our approach.

DETECTION OF PERSONS IN MLS POINT CLOUDS

Abstract. In this paper we present an approach for the detection of persons in point clouds gathered by mobile laser scanning (MLS) systems. The approach consists of a preprocessing and the actual detection. The main task of the preprocessing is to reduce the amount of data which has to be processed by the detection. To fulfill this task, the preprocessing consists of ground removal, segmentation and several filters. The detection is based on an implicit shape models (ISM) approach which is an extension to bag-of-words approaches. For this detection method, it is sufficient to work with a small amount of training data. Although in this paper we focus on the detection of persons, our approach is able to detect multiple classes of objects in point clouds. Using a parameterization of the approach which offers a good compromise between detection and runtime performance, we are able to achieve a precision of 0.68 and a recall of 0.76 while having a average runtime of 370 ms per single scan rotation of the rotating head of a typical MLS sensor.

AN AUTOMATED ROAD ROUGHNESS DETECTION FROM MOBILE LASER SCANNING DATA

Abstract. Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.