Terrain Points Research Articles

LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain

When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.

Point cloud filtering on UAV based point cloud

Nowadays, Unmanned Aerial Vehicles (UAVs) have been attracted wide attentions such as a new measurement equipment and mapping, which are capable of the high-resolution point cloud data collection. In addition, a massive point cloud data has brought about the data filtering and irregular data organization for the generation of digital terrain models. Filtering of point clouds contains vegetations and artificial objects play a crucial role for bare earth terrain modelling. Topographical maps rely on the data structures which are built on bare ground terrain points. The bare earth surface extraction is not the only crucial to the topographical maps but also decision-making processes such as natural hazards management, deformation analysis and interpretation.In order to filter a UAV-based 3D raw point cloud data, in this paper, filtering performance of four different algorithms using open source and commercial software’s have been investigated, (1) curvature based (Multiscale Curvature Classification-MCC), (2) surface-based filtering (FUSION), (3) progressive TIN based (LasTool-LasGround module-commercial) and (4) physical simulation processing (Cloth Simulation Filtering-CSF). The applied filtering results were validated with the reference data set classified by operator. Although different filtering methodologies implemented on point clouds, these methods demonstrated similar results to extract ground on distinctive terrain feature such as dense vegetated, flat surface, rough and complex landscapes. The filtering algorithms’ results revealed that UAV-generated data suitable for extraction of bare earth surface feature on the different type of a terrain. Accuracy of the filtered point cloud reached the 93% true classification on flat surfaces from CSF filtering method.

The use of triangulation processing algorithms for the construction of combined model of the underwater and above-water terrain of the bed of the Bratsk Reservoir

The article summarizes the experience gained during processing of the data about the terrain of the Bratsk Reservoir bed. We discuss the available sources of information on the land and underwater terrain and justify the choice of the sources used in the project. To measure the actual depths in the regions of interest an echo-sounder of the consumer grade was applied, and we consider the processing of its measurements in some details. We describe the free, open source and custom software, which we apply for the processing of the data. We consider the use of the several algorithms that we have developed for processing of the terrain points. The main goal of this processing is to obtain the combined terrain model, which summarizes the data from the various information sources: the topographic maps, the pilot charts and the echo sounder measurements. All the algorithms considered build and process the triangulations of irregular sets of points. The algorithms used are: constrained Delaunay triangulation construction; removal of artefacts of triangulation, constructed from the terrain isolines; map morphing to reconcile the terrain models: and triangulation fragment replacement. The resulting combined terrain model contains all the information about the terrain in the area of interest and allows to use the model for the further hydrological calculations.

Radial basis function network‐based available measurement classification of interferometric radar altimeter for terrain‐aided navigation

The purpose of this study is to propose a measurement classification method necessary to implement precision terrain-aided navigation (TAN) by using an interferometric radar altimeter (IRA) as a technology that can replace global positioning system/inertial navigation system integrated navigation. IRA is a sensor that extracts the angle perpendicular to the direction of flight, the look angle, and the slant range from the aircraft to the nearest terrain point. Unlike the radio altimeter which only measures the direct downward distance, IRA can be converted into three-dimensional coordinates in the navigation system. However, the IRA output has a disadvantage that it has uncertainty that cannot be predicted due to the signal processing and environmental factors. Therefore, a useful navigation technique for classifying sensor outputs is needed to implement precision TAN. This study introduces the radial basis function network and extreme learning machine methods to classify available IRA measurements and verifies the suitability of the proposed classification method by applying it to the bank of Kalman filter) and particle filter-based TAN.

Progressive Filtering of Airborne LiDAR Point Clouds Using Graph Cuts

The development of robust and accurate filtering approaches for automated extraction of digital terrain models (DTMs) from airborne Light Detection and Ranging (LiDAR) data continues to be a challenge. The problem is due to the nature of LiDAR point clouds, the complexity of scene components, and the intrinsic structure of the terrain itself. This paper proposes a novel approach for filtering LiDAR point clouds, which exploits the spatial structure of the terrain and the spatial coherence among the LiDAR points. Terrain points are progressively detected through energy minimization using graph cuts. The energy function and graph model encode both pointwise closeness and pairwise smoothness. The DTM is then extracted through progressive filtering via the graph cuts. The performance of the proposed method is investigated using two datasets with different point densities, terrain complexity, and land covers. The results show that the filter can effectively remove nonterrain points, leading to an accurately extracted DTM. The filter is also compared with other methods reported in the literature, the comparison demonstrating that the proposed method exhibits advantages in terms of performance.

VEGETATION REMOVAL FROM UAV DERIVED DSMS, USING COMBINATION OF RGB AND NIR IMAGERY

Abstract. Current advancements on photogrammetric software along with affordability and wide spreading of Unmanned Aerial Vehicles (UAV), allow for rapid, timely and accurate 3D modelling and mapping of small to medium sized areas. Although the importance and applications of large format aerial overlaps cameras and photographs in Digital Surface Model (DSM) production and LIDAR data is well documented in literature, this is not the case for UAV photography. Additionally, the main disadvantage of photogrammetry is the inability to map the dead ground (terrain), when we deal with areas that include vegetation. This paper assesses the use of near-infrared imagery captured by small UAV platforms to automatically remove vegetation from Digital Surface Models (DSMs) and obtain a Digital Terrain Model (DTM). Two areas were tested, based on the availability of ground reference points, both under trees and among vegetation, as well as on terrain. In addition, RGB and near-infrared UAV photography was captured and processed using Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms to generate DSMs and corresponding colour and NIR orthoimages with 0.2 m and 0.25 m as pixel size respectively for the two test sites. Moreover, orthophotos were used to eliminate the vegetation from the DSMs using NDVI index, thresholding and masking. Following that, different interpolation algorithms, according to the test sites, were applied to fill in the gaps and created DTMs. Finally, a statistic analysis was made using reference terrain points captured on field, both on dead ground and under vegetation to evaluate the accuracy of the whole process and assess the overall accuracy of the derived DTMs in contrast with the DSMs.

A VOXEL-BASED FILTERING ALGORITHM FOR MOBILE LIDAR DATA

Abstract. This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data

Transmission line corridor (i.e., Right-of-Ways (ROW)) clearance management plays a critically important role in power line risk management and is an important task of the routine power line inspection of the grid company. The clearance anomaly detection measures the distance between the power lines and the surrounding non-power-facility objects in the corridor such as trees, and buildings, to judge whether the clearance is within the safe range. To find the clearance hazards efficiently and flexibly, this study thus proposed an automatic clearance anomaly detection method utilizing LiDAR point clouds collected by unmanned aerial vehicle (UAV). Firstly, the terrain points were filtered out using two-step adaptive terrain filter and the pylons were detected in the non-terrain points following a feature map method. After dividing the ROW point clouds into spans based on the pylon detection results, the power line point clouds were extracted according to their geometric distribution in local span point clouds slices, and were further segmented into clusters by applying conditional Euclidean clustering with linear feature constraints. Secondly, the power line point clouds segments were iteratively fitted with 3D catenary curve model that is represented by a horizontal line and a vertical catenary curve defined by a hyperbolic cosine function, resulting in a continuous mathematical model of the discretely sampled points of the power line. Finally, a piecewise clearance calculation method which converts the point-to-catenary curve distance measurements to minimal distance calculation based on differential geometry was used to calculate the distance between the power line and the non-power-facility objects in the ROW. The clearance measurements were compared with the standard safe threshold to find the clearance anomalies in the ROWs. Multiple LiDAR point clouds datasets collected by a large-scale UAV power line inspection system were used to validate the effectiveness and accuracy of the proposed method. The detected results were validated through qualitatively visual inspection, quantitatively manual measurements in raw point clouds and on-site field survey. The experiments show that the automatic clearance anomaly detection method proposed in this paper effectively detects the clearance hazards such as tree encroachment, and the clearance measurement accuracy is decimeter level for the LiDAR point clouds collected by our UAV inspection system.

A Microtopographic Feature Analysis-Based LiDAR Data Processing Approach for the Identification of Chu Tombs

Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS) algorithm that we developed for the classification of archaeological objects and terrain points and the detection of archaeological remains. The archaeological features and patterns are interpreted and extracted from LiDAR point cloud data to construct an archaeological object pattern database. A microtopographic factor is calculated based on the archaeological object patterns, and this factor converts the massive point cloud data into a raster feature image. A fuzzy clustering algorithm based on the archaeological object patterns is presented for raster feature image segmentation and the detection of archaeological remains. Using the proposed approach, we investigated four typical areas with different types of Chu tombs in Central China, which had dense vegetation and high population densities. Our research results show that the proposed LiDAR data processing approach can identify archaeological remains from large-volume and massive LiDAR data, as well as in areas with dense vegetation and trees. The studies of different archaeological object patterns are important for improving the robustness of the proposed APoPCS algorithm for the extraction of archaeological remains.

An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

In this study, full-waveform LiDAR data were exploited to detect weak returns backscattered by the bare terrain underneath vegetation canopies and thus improve the generation of a digital terrain model (DTM). Building on the methods of progressive generation of triangulation irregular network (TIN) model reported in the literature, we proposed an integrated approach where echo detection, terrain identification, and TIN generation were carried out iteratively. The proposed method was tested on a dataset collected by a Riegl LMS Q-560 scanner over a study area near Sault Ste. Marie, Ontario, Canada (46°33′56′′N, 83°25′18′′W). The results demonstrated that more terrain points under shrubs could be identified, and the generated DTMs exhibited more details in the terrain than those obtained using the progressive TIN method. In addition, 1275 points across this study area were surveyed on the ground and used to validate the proposed approach. The estimated elevations were shown to have a strong linear relationship with the measured ones, with R2 values above 0.98, and the RMSEs (Root Mean Squared Errors) between them were less than 0.15 m even for areas with hilly terrains underneath vegetation canopies.

Compression Rates and Spatial Judgment Biases Made from Synthetic Vision Perspective Displays

In this work, the relationship between error in human spatial judgments made from perspective displays with the compression rates used to represent the judged spatial quantities was investigated. Two-dimensional perspective displays are often used to represent three-dimensional information to humans. Such displays can use different compression rates (actual distance conveyed per unit of screen distance) for identical spatial quantities. An existing data set was used, in which spatial judgments (relative distance, angle, and elevation) were made about the relative location of a terrain point shown on a simulated aircraft synthetic vision systems display. The correlations between compression rates and associated judgment error were then measured. Correlations were computed for average participant judgments as well as for each participant, from which an average correlation was computed. Significant negative correlations were found between compression rates and judgment error for all of the analyzed spatial judgments. These were particularly strong for relative distance judgment error. There were noticeable differences between the correlations of averages and the associated average correlations. Results indicate that compression rates bias the analyzed spatial judgments. However, there are significant individual differences in how the compression rates appear to impact judgment error.

AN EFFICIENT METHOD TO CREATE DIGITAL TERRAIN MODELS FROM POINT CLOUDS COLLECTED BY MOBILE LiDAR SYSTEMS

Abstract. The digital terrain models (DTM) assume an essential role in all types of road maintenance, water supply and sanitation projects. The demand of such information is more significant in developing countries, where the lack of infrastructures is higher. In recent years, the use of Mobile LiDAR Systems (MLS) proved to be a very efficient technique in the acquisition of precise and dense point clouds. These point clouds can be a solution to obtain the data for the production of DTM in remote areas, due mainly to the safety, precision, speed of acquisition and the detail of the information gathered. However, the point clouds filtering and algorithms to separate “terrain points” from “no terrain points”, quickly and consistently, remain a challenge that has caught the interest of researchers. This work presents a method to create the DTM from point clouds collected by MLS. The method is based in two interactive steps. The first step of the process allows reducing the cloud point to a set of points that represent the terrain’s shape, being the distance between points inversely proportional to the terrain variation. The second step is based on the Delaunay triangulation of the points resulting from the first step. The achieved results encourage a wider use of this technology as a solution for large scale DTM production in remote areas.

Automated extraction of 3D vector topographic feature line from terrain point cloud

This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm.

AUTOMATIC TREE DATA REMOVAL METHOD FOR TOPOGRAPHY MEASUREMENT RESULT USING TERRESTRIAL LASER SCANNER

Abstract. Recently, laser scanning has been receiving greater attention as a useful tool for real-time 3D data acquisition, and various applications such as city modelling, DTM generation and 3D modelling of cultural heritage sites have been proposed. And, former digital data processing were demanded in the past digital archive techniques for cultural heritage sites. However, robust filtering method for distinguishing on- and off-terrain points by terrestrial laser scanner still have many issues. In the past investigation, former digital data processing using air-bone laser scanner were reported. Though, efficient tree removal methods from terrain points for the cultural heritage are not considered. In this paper, authors describe a new robust filtering method for cultural heritage using terrestrial laser scanner with "the echo digital processing technology" as latest data processing techniques of terrestrial laser scanner.

A Hybrid Vehicle Extraction Approach from Low-Quality LiDAR Data Based on Robust One-Class Support Vector Machine

Vehicle extraction becomes possible as the high-performance airborne light detection and ranging (LiDAR) systems can offer very dense and accurate point cloud, which means the sophisticated objects can be recorded in detail, combined with color information from airborne image, hyperspectral and intensity. However, few studies have investigated in extracting vehicles from LiDAR data only, especially when its quality is low, which is the main difficulty for most LiDAR applications. In this paper, a hybrid approach has been proposed to extract vehicles from low-quality LiDAR data. In order to extract vehicle from low-resolution LiDAR data, a robust one-class support vector machine-minimum covariance determinant (OCSVM-MCD) is proposed based on a multivariate dispersion estimator and weighted strategy. Firstly, the three-dimensional (3D) point dataset is classified into nonterrain and terrain points with progressive morphological filter with a slight improvement. Secondly, nonterrain points are segmented by clustering technique and missing blobs are searched from terrain points. Then, the vehicles are extracted from clustering and searching results by OCSVM-MCD, and a hybrid principle is put forward to improve the extraction result at last. The proposed method has been evaluated with two benchmark datasets from ISPRS, and proved that by the method, most vehicles can be extracted from low-quality LiDAR data with an encouraging result.

Vegetation filtering algorithm for UAV-borne lidar point clouds: a case study in the middle-lower Yangtze River riparian zone

ABSTRACTA vegetation filtering algorithm is proposed for unmanned aerial vehicle (UAV)-borne lidar point clouds collected in the middle-lower Yangtze River riparian zone covered with multilayer and high-density vegetation. The proposed algorithm aims at generating digital elevation model, which consists of the following steps. First, multi-echo analysis is adopted for coarse filtering of point clouds. Then, morphological calculation is used to extract the ground seed points. Next, the trend surface of the local terrain is fitted. Meanwhile, the vegetation points are removed with the ground points preserved via random sample consensus. Experiments demonstrate that the performances of the algorithm were more accurate than Terrasolid’s TerraScan when dealing with UAV-borne lidar point clouds in limited sample of a multilayer and high-density vegetation covering areas. The research results also show that by using a special filtering approach it is possible to classify laser points into terrain and vegetation automatically even for thoroughly mixed vegetation and terrain points and penetration rate of below 15%.

How do ICP variants perform when used for scan matching terrain point clouds?

Many variants of the Iterative Closest Point (ICP) algorithm have been proposed for registering point clouds. This paper explores the performance of 20,736 ICP variants applied to the registration of point clouds for the purpose of terrain mapping, using data obtained from a mobile platform. The methodology of the study has involved taking sequences of 100 consecutive scans at three distinct scenes along the route of a mining haul truck operating in a typical surface mining environment. The scan sequences were obtained at 20 Hz from a Velodyne HDL-64E mounted on the truck. The aim is to understand how well the ICP variants perform in consolidating these scans into sub-maps. Variants are compared against three metrics: accuracy, precision, and relative computational cost. The main finding of the paper is that none of the variants is simultaneously accurate, precise, and fast to compute, across all three scenes. The best performing variants employed strategies that filtered the data sets, used local surface geometry in the form normals, and used the distance between points in one point cloud to a corresponding surface from a reference point cloud as a measure of the fit between two point clouds. The significance of this work is that it: (i) provides guidance in the construction of ICP variants for terrain mapping; and (ii) identifies the significant limitations of existing ICP variants for this application.

STREET-SCENE TREE SEGMENTATION FROM MOBILE LASER SCANNING DATA

Our work addresses the problem of extracting trees from mobile laser scanning data. The work is a two step-wise strategy, including terrain point removal and tree segmentation. First, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, a tree segmentation is presented to extract individual trees via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree segmentation method. Qualitative analysis shows that our algorithm achieves a good performance.

STREET-SCENE TREE SEGMENTATION FROM MOBILE LASER SCANNING DATA

Abstract. Our work addresses the problem of extracting trees from mobile laser scanning data. The work is a two step-wise strategy, including terrain point removal and tree segmentation. First, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, a tree segmentation is presented to extract individual trees via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree segmentation method. Qualitative analysis shows that our algorithm achieves a good performance.

A MIN-CUT BASED FILTER FOR AIRBORNE LIDAR DATA

LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the points or grids, such as the slope, and elevation discontinuities. Since considering only the local properties restricts the filtering performance due to the complexity of the terrain and the features, some recent methods utilize global properties of the terrain as well. This paper presents a new ground filtering method, Min-cut Based Filtering (MBF), which takes both local and global properties of the points into account. MBF considers ground filtering as a labeling task. First, an energy function is designed on a graph, where LiDAR points are considered as the nodes on the graph that are connected to each other as well as to two auxiliary nodes representing ground and off-ground labels. The graph is constructed such that the data costs are assigned to the edges connecting the points to the auxiliary nodes, and the smoothness costs to the edges between points. Data and smoothness terms of the energy function are formulated using point elevations and approximate ground information. The data term conducts the likelihood of the points being ground or off-ground while the smoothness term enforces spatial coherence between neighboring points. The energy function is optimized by finding the minimum-cut on the graph via the alpha-expansion algorithm. The resulting graph-cut provides the labeling of the point cloud as ground and off-ground points. Evaluation of the proposed method on the ISPRS test dataset for ground filtering demonstrates that the results are comparable with most current existing methods. An overall average filtering accuracy for the 15 ISPRS test areas is 91.3%.