Simulated Point Clouds Research Articles

Model-free pose estimation using point cloud data

A model-free pose (relative attitude and position) estimation process using point cloud data is developed to support envisioned autonomous satellite servicing missions. Unlike model-based pose estimation that typically requires a three-dimensional geometric model of a satellite to be serviced, the model-free pose estimation presented in this paper employs object features detected in situ using point cloud data. Its overall process is similar to existing model-free pose estimation algorithms in that it requires edge detection, object boundary identification, edge vector determination to form an orthogonal triad for attitude estimation, and object area and centroid calculation for position estimation with respect to the sensor. However, significant improvements have been made toward fully autonomous, model-free pose estimation by employing the Hough transform that allows robust line fitting of jagged edges resulting from object rotations and the discrete nature of point cloud data. In addition, a plane fitting algorithm using the homogeneous transformation was developed to compute plane normal vectors to help improve attitude estimation accuracy. Using both measured and simulated point cloud data of test objects, the performance of the developed model-free pose estimation process was examined. It was able to produce accurate position and attitude of the objects. Future effort will be needed to include more general geometric features such as circles, ellipses, and arcs to provide capabilities needed toward fully autonomous model-free pose estimation process.

Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest

Leaf inclination angle and leaf angle distribution (LAD) are important plant structural traits, influencing the flux of radiation, carbon and water. Although leaf angle distribution may vary spatially and temporally, its variation is often neglected in ecological models, due to difficulty in quantification.In this study, terrestrial LiDAR (TLS) was used to quantify the LAD variation in natural European beech (Fagus Sylvatica) forests. After extracting leaf points and reconstructing leaf surface, leaf inclination angle was calculated automatically. The mapping accuracy when discriminating between leaves and woody material was very high across all beech stands (overall accuracy = 87.59%). The calculation accuracy of leaf angles was evaluated using simulated point cloud and proved accurate generally (R2 = 0.88, p < 0.001; RMSE = 8.37°; nRMSE = 0.16). Then the mean (θmean), mode (θmode), and skewness of LAD were calculated to quantify LAD variation.Moderate variation of LAD was found in different successional status stands (θmean∈ [36.91°, 46.14°], θmode∈ [17°, 43°], skewness ∈ [0.07, 0.48]). Rather than the previously assumed spherical distribution or reported planophile distribution, here we find that LAD tended towards a uniform distribution in young and medium stands, and a planophile distribution in mature stands. A strong negative correlation was also found between plot θmean and plot median canopy height, making it possible to estimate plot specific LAD from canopy height data.Larger variation of LAD was found on different canopy layers (θmean∈ [33.64°, 52.97°], θmode∈ [14°, 64°], skewness ∈ [−0.30, 0.71]). Beech leaves grow more vertically in the top layer, while more obliquely or horizontally in the middle and bottom layer.LAD variation quantified by TLS can be used to improve leaf area index mapping and canopy photosynthesis modelling.

Ground Surface Filtering of 3D Point Clouds Based on Hybrid Regression Technique

Lidar has received a lot of attention due to its precise ranging accuracy. Ground points filtering is an important task in point clouds processing. It’s a challenge to model the ground surface and filter the point clouds accurately in the case of complex ground undulations, occlusions, and sparse point clouds. A novel ground surface modeling method based on a hybrid regression technique is proposed in this paper. The method integrates Gaussian process regression (GPR) and robust locally weighted regression (RLWR) by dividing the point clouds that are projected on the polar grid map into radial and circumferential filtering processes to form a hybrid regression model, which has the ability to eliminate the influence of outliers and model the ground surface robustly. First, the RLWR combined with gradient filter is applied to fit the sampled points in the radial direction, which will exclude outliers and get the fitting ground line. All radial fitting lines constitute the seed skeleton of the whole plane. Then, based on the seeds in the same circumferential of the skeleton, the GPR is applied to construct the ground surface model. The comparative experiments are implemented quantitatively and qualitatively on the simulated point clouds and measured data. The results show that the proposed method performs well in most real scenarios, even in the cases of ground undulation, occlusion, and sparse point clouds.

A new 3D space syntax metric based on 3D isovist capture in urban space using remote sensing technology

Visibility represents the size of a space that people visually perceive. An isovist is a spatial concept and measure to illustrate visibility and its visual characteristics in the built environment. For measuring openness within an urban area, the isovist has been widely used as a representative measure. Since actual perception of urban space is based on the three-dimensional (3D) form, the conventional two-dimensional analysis of an isovist has been sought to extend to 3D isovist measures. In this paper, we define a new measure for a 3D urban environment that is directly applicable to a standard range sensor measurement. Upon developing volumetric and angular openness indexes in the three-dimensional coordinate systems, the findings in this paper emphasize that the viewing angle is critical in measuring 3D openness while closely connected to the range measurements. By mutually considering the angle and the range simultaneously, the proposed 3D isovist captures a viewpoint from pedestrians for more realistic openness evaluation. Additionally, we validate the proposed method in both simulated environments and real point cloud sensor data. The proposed, straightforward, point-based method is expected to be applicable to measure a real urban environment with a mobile mapping system with 3D point clouds and LiDAR.

Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data

Many studies have been focusing on reconstructing the branch skeleton of a three-dimensional (3D) tree structure that is based on photos or point clouds scanned by a terrestrial laser scanner (TLS), but leaves, as the important component of a tree, are often ignored or simplified because of their complexity. Therefore, we develop a voxel-based method to add leaves to a reconstructed 3D branches structure based on TLS point clouds. The location and size of each leaf depend on the spatial distribution and density of leaves points in the voxel. We reconstruct a small 3D scene with four realistic 3D trees and a virtual tree (including trunk, branches, and leaves), and validate the structure of each tree through the directional gap fractions calculated based on simulated point clouds of this reconstructed scene by the ray-tracing algorithm. The results show good coherence with those from measured point clouds data. The relative errors of the directional gap fractions are no more than 4.1%, though the method is limited by the effects of point occlusion. Therefore, this method is shown to give satisfactory consistency both visually and in the quantitative evaluation of the 3D structure.

Determination of Minimum Detectable Deformation of Terrestrial Laser Scanning Based on Error Entropy Model

Terrestrial laser scanning (TLS) is a widely used remote sensing technique which can produce very dense point cloud data very promptly and is particularly suited for surface deformation monitoring. Deformation magnitude is typically estimated by comparing TLS scans over the same area but at different time epochs of interest. However, there is an issue related to such a method, which is not clear that whether the difference between two successive surveys results from the surface deformation. Hence, it is vital to determine the minimum detectable deformation (MDD) by a TLS device with a given registration and point cloud error level. In this paper, the MDD is determined based on the computation of the point cloud error entropy. The performance of the proposed method is extensively evaluated numerically using simulated plane board deformation point clouds under a range of distances and incidence angles. This proposed method was also successfully applied to deformation monitoring of one landslide test site located in the Wuhan University of Technology. The experimental results demonstrate that the theoretical MDD has a good match with the actual deformation, and the deformation greater than MDD can be accurately detected by the TLS device.

Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner

Terrestrial laser scanners (TLS) have demonstrated great potential in estimating structural attributes of forest canopy, such as leaf area index (LAI). However, the inversion accuracy of LAI is highly dependent on the measurement configuration of TLS and spatial characteristics of the scanned tree. Therefore, a modified gap fraction model integrating the path length distribution is developed to improve the accuracy of retrieved single-tree leaf area (LA) by considering the shape of a single-tree crown. The sensitivity of TLS measurement configurations on the accuracy of the retrieved LA is also discussed by using the modified gap fraction model based on several groups of simulated and field-measured point clouds. We conclude that (1) the modified gap fraction model has the potential to retrieve LA of an individual tree and (2) scanning distance has the enhanced impact on the accuracy of the retrieved LA than scanning step. A small scanning step for broadleaf trees reduces the scanning time, the storage volume, and postprocessing work in the condition of ensuring the accuracy of the retrieved LA. This work can benefit the design of an optimal survey configuration for the field campaign.

Pose estimation and tracking of non-cooperative rocket bodies using Time-of-Flight cameras

This paper presents a methodology for estimating the position and orientation of a rocket body in orbit – the target – undergoing a roto-translational motion, with respect to a chaser spacecraft, whose task is to match the target dynamics for a safe rendezvous. During the rendezvous maneuver the chaser employs a Time-of-Flight camera that acquires a point cloud of 3D coordinates mapping the sensed target surface. Once the system identifies the target, it initializes the chaser-to-target relative position and orientation. After initialization, a tracking procedure enables the system to sense the evolution of the target's pose between frames. The proposed algorithm is evaluated using simulated point clouds, generated with a CAD model of the Cosmos-3M upper stage and the PMD CamCube 3.0 camera specifications.

Boundary points based scale invariant 3D point feature

In this paper, we propose a method for encoding scale invariant 3D point features. We extract a set of boundary points from a point cloud. Next, we apply the scale-space concept on the boundary points to detect the scale invariant point border. We confirm three orthometric axes as the local reference frames. Three distribution matrices are generated by implementing the strategy of SPIN image method, and one-row-vector of descriptors are finally calculated. Experimental results on simulated and real scene point clouds demonstrate that the scale-invariant features of 3D point clouds can be effectively encoded by our method.

Scaling iterative closest point algorithm using dual number quaternions

This paper presents a novel extended iterative closest point algorithm which can handle 3D point sets registration problem with scaling factor. In each iterative process of this algorithm, a scaling factor is introduced into objective function used to estimate all the transformation parameters, and this paper succeeds in solving this objective function using dual number quaternions. If the corresponding point pairs have been established properly, this algorithm can give all the global optimal transformation parameters by one-step calculation rather than iterative process. This algorithm is independent of shape representation and feature extraction, and thereby it is general for scaling registration of 3D point sets. The effectiveness of proposed algorithm is demonstrated in several experiments on simulated 3D curves and real 3D point clouds. Furthermore, the execution time of proposed algorithm is analyzed by a comparative experiment. The final results demonstrate that this algorithm can successfully handle 3D point sets registration problem with scaling factor, and this algorithm has a better performance in the aspect of running speed for a big size of 3D point sets.

Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis.

Autoassociative neural networks (ANNs) have been proposed as a nonlinear extension of principal component analysis (PCA), which is commonly used to identify linear variation patterns in high-dimensional data. While principal component scores represent uncorrelated features, standard backpropagation methods for training ANNs provide no guarantee of producing distinct features, which is important for interpretability and for discovering the nature of the variation patterns in the data. Here, we present an alternating nonlinear PCA method, which encourages learning of distinct features in ANNs. A new measure motivated by the condition of orthogonal loadings in PCA is proposed for measuring the extent to which the nonlinear principal components represent distinct variation patterns. We demonstrate the effectiveness of our method using a simulated point cloud data set as well as a subset of the MNIST handwritten digits data. The results show that standard ANNs consistently mix the true variation sources in the low-dimensional representation learned by the model, whereas our alternating method produces solutions where the patterns are better separated in the low-dimensional space.

SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

Abstract. The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

Point Cloud Based Relative Pose Estimation of a Satellite in Close Range.

Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective.

TH-AB-202-08: A Robust Real-Time Surface Reconstruction Method On Point Clouds Captured From a 3D Surface Photogrammetry System

Purpose: Surface photogrammetry (e.g. VisionRT, C-Rad) provides a noninvasive way to obtain high-frequency measurement for patient motion monitoring in radiotherapy. This work aims to develop a real-time surface reconstruction method on the acquired point clouds, whose acquisitions are subject to noise and missing measurements. In contrast to existing surface reconstruction methods that are usually computationally expensive, the proposed method reconstructs continuous surfaces with comparable accuracy in real-time. Methods: The key idea in our method is to solve and propagate a sparse linear relationship from the point cloud (measurement) manifold to the surface (reconstruction) manifold, taking advantage of the similarity in local geometric topology in both manifolds. With consistent point cloud acquisition, we propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, building the point correspondences by the iterative closest point (ICP) method. To accommodate changing noise levels and/or presence of inconsistent occlusions, we further propose a modified sparse regression (MSR) model to account for the large and sparse error built by ICP, with a Laplacian prior. We evaluated our method on both clinical acquired point clouds under consistent conditions and simulated point clouds with inconsistent occlusions. The reconstruction accuracy was evaluated w.r.t. root-mean-squared-error, by comparing the reconstructed surfaces against those from the variational reconstruction method. Results: On clinical point clouds, both the SR and MSR models achieved sub-millimeter accuracy, with mean reconstruction time reduced from 82.23 seconds to 0.52 seconds and 0.94 seconds, respectively. On simulated point cloud with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent performance despite the introduced occlusions. Conclusion: We have developed a real-time and robust surface reconstruction method on point clouds acquired by photogrammetry systems. It serves an important enabling step for real-time motion tracking in radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.

A multi‐scale plane‐detection method based on the Hough transform and region growing

AbstractThe detection of planes from 3D point clouds plays an important role in fields such as 3D modelling, rock mechanics, registration and robotics. A multi‐scale plane‐detection method is proposed, based on extracting growth units using the Hough transform and subsequent region growing into actual plane boundaries. Because the approximate geometric features of the original plane area can be obtained from the growth units, discriminant conditions for adaptive plane growing could be provided for the growing stage. In order to detect physical planes at different scales, a strategy of multi‐scale normal estimation is introduced. Simulated icosahedron point clouds, together with actual engineering point clouds, were used to test the performance of the proposed method. Experimental results show that planar point clouds could be detected effectively, especially with rock‐mass engineering applications.

Robust Locally Weighted Regression Techniques for Ground Surface Points Filtering in Mobile Laser Scanning Three Dimensional Point Cloud Data

This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: $x-z$ and $y-z$ . The $z$ (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted $z$ values, along with the corresponding $x$ (or $y$ ) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for $z$ values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy.

Measurement Axis Searching Model for Terrestrial Laser Scans Registration

Nowadays, terrestrial Lidar scans can cover rather a large area; the point densities are strongly varied because of the line-of-sight measurement principle in potential overlaps with scans taken from different viewpoints. Most of the traditional methods focus on registration algorithm and ignore searching model. Sometimes the traditional methods are directly used to align two point clouds; a large critically unsolved problem of the large biases will be created in areas distant from the overlaps while the local overlaps are often aligned well. So a novel measurement axis searching model (MASM) has been proposed in this paper. The method includes four steps:(1)the principal axis fitting,(2)the measurement axis generation,(3)low-high-precision search, and(4)result generation. The principal axis gives an orientation to the point cloud; the search scope is limited by the measurement axis. The point cloud orientation can be adjusted gradually until the achievement of the global optimum using low- and high-precision search. We perform some experiments with simulated point clouds and real terrestrial laser scans. The results of simulated point clouds have shown the processing steps of our method, and the results of real terrestrial laser scans have shown the sensitivity of the approach with respect to the indoor and outdoor scenes.

Using geometry tessellation and Markov chain Monte Carlo for segmentation of LiDAR point cloud data

This paper presents a segmentation algorithm of LiDAR point cloud data by which geometric and distributional features of objects are extracted. In this proposed algorithm, each object is considered to occupy a statistically homogeneous region and its acquired elevations are modeled as a normal distribution. To segment the LiDAR point cloud into homogeneous regions, a Voronoi tessellation is first used to partition its domain into polygons. The number of polygons is given in practice. Each of the polygons is assigned a random label variable to indicate the region to which it belongs. By Bayesian inference, the joint probability of labels and distribution parameters conditional on the given dataset can be obtained up to a normalizing constant. A Markov chain Monte Carlo scheme is designed to simulate from the posterior and to estimate the model parameters. Finally, the optimal segmentation is obtained under maximum <italic<a posteriori</italic< estimation. Experiments on real point cloud data show that normal distribution parameters for each region quickly converge to their stable values, and the optimal segmentation results can be obtained within 20,000 iterations for all datasets. Experiments on simulated point cloud data demonstrate that the proposed algorithm can accurately estimate the model parameters.

Scale alignment of 3D point clouds with different scales

In this paper, we propose two methods for estimating the scales of point clouds to align them. The first method estimates the scale of each point cloud separately: each point cloud has its own scale that is something like the size of a scene. We call it a keyscale, which is a representative scale and is defined for a given 3D point cloud as the minimum of the cumulative contribution rates of PCA of descriptors over different scales. Our second method directly estimates the ratio of scales (scale ratio) of two point clouds. Instead of finding the minimum, this approach registers the two sets of curves of the cumulative contribution rate of PCA by assuming that those differ only in scale. Experimental results with simulated and real scene point clouds demonstrate that the scale alignment of 3D point clouds can be effectively accomplished by our scale ratio estimation.