Registration Of Laser Scans Research Articles

Fast registration of forest terrestrial laser scans using key points detected from crowns and stems

ABSTRACT Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. Most existing registration methods rely on stems to determine the transformation parameters. However, the complexity of the registration problem increases dramatically as the number of stems grows. It is tricky to reduce the stems and determine the valid ones that can provide reliable registration transformation without a knowledge of the two scans. This paper presents an automatic and fast registration of TLS point clouds in forest areas. It reduces stems by selecting from the overlap areas, which are recovered from the mode-based key points that are detected from crowns. The proposed method was tested in a managed forest in Finland, and was compared with the stem-based registration method without reducing stems. The experiments demonstrated that the mean rotation error was 2.09′, and the mean errors in horizontal and vertical translation were 1.13 and 7.21 cm, respectively. Compared with the stem-based method, the proposed method improves the registration efficiency significantly (818 s vs 96 s) and achieves similar results in terms of the mean registration errors (1.94′ for rotation error, 0.83 and 7.38 cm for horizontal and vertical translation error, respectively).

NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement

Accurate registration of light detection and ranging (LiDAR) point clouds and images is a prerequisite for integrating the spectral and geometrical information collected by low-cost unmanned aerial vehicle (UAV) systems. Most registration approaches take the directly georeferenced LiDAR point cloud as a rigid body, based on the assumption that the high-precision positioning and orientation system (POS) in the LiDAR system provides sufficient precision, and that the POS errors are negligible. However, due to the large errors of the low-precision POSs commonly used in the low-cost UAV LiDAR systems (ULSs), dramatic deformation may exist in the directly georeferenced ULS point cloud, resulting in non-rigid transformation between the images and the deformed ULS point cloud. As a result, registration may fail when using a rigid transformation between the images and the directly georeferenced LiDAR point clouds. To address this problem, we proposed NRLI-UAV, which is a non-rigid registration method for registration of sequential raw laser scans and images collected by low-cost UAV systems. NRLI-UAV is a two-step registration method that exploits trajectory correction and discrepancy minimization between the depths derived from structure from motion (SfM) and the raw laser scans to achieve LiDAR point cloud quality improvement. Firstly, the coarse registration procedure utilizes global navigation satellite system (GNSS) and inertial measurement unit (IMU)-aided SfM to obtain accurate image orientation and corrects the errors of the low-precision POS. Secondly, the fine registration procedure transforms the original 2D-3D registration to 3D-3D registration. This is performed by setting the oriented images as the reference, and iteratively minimizing the discrepancy between the depth maps derived from SfM and the raw laser scans, resulting in accurate registration between the images and the LiDAR point clouds. In addition, an improved LiDAR point cloud is generated in the mapping frame. Experiments were conducted with data collected by a low-cost UAV system in three challenging scenes to evaluate NRLI-UAV. The final registration errors of the images and the LiDAR point cloud are less than one pixel in image space and less than 0.13 m in object space. The LiDAR point cloud quality was also evaluated by plane fitting, and the results show that the LiDAR point cloud quality is improved by 8.8 times from 0.45 m (root-mean-square error [RMSE] of plane fitting) to 0.05 m (RMSE of plane fitting) using NRLI-UAV, demonstrating a high level of automation, robustness, and accuracy.

Decreasing the Uncertainty of the Target Center Estimation at Terrestrial Laser Scanning by Choosing the Best Algorithm and by Improving the Target Design

During the registration and georeferencing of terrestrial laser scans, it is common to use targets to mark discrete points. To improve the accuracy of the registration, the uncertainties of the target center estimation (TCE) have to be minimized. The present study examines different factors influencing the precision of the TCE. Here, the focus is on the algorithm and the target design. It is determined that, in general, the uncertainties of the TCE are much smaller than those indicated by the manufacturers. By comparing different algorithms for the first time, it was possible to clearly determine that an algorithm using image correlations yields the smallest standard deviations for the TCE. A comparison of different target designs could not identify an ideal commercially available target. For this reason, a new target, the BOTA8 (BOnn TArget with 8-fold pattern) was developed, which leads to smaller standard deviations than the previous targets. By choosing the best algorithm and improving the target design, standard deviations of 0.5 mm in distance direction and 1.2 arcsec in angular direction for a scan distance up to 100 m were achieved with the laser scanner Leica ScanStation P20. The uncertainties could be reduced by several millimetres and angular seconds compared to the manufacturer’s targets and software.

Integration of geomatics methodologies and creation of a cultural heritage app using augmented reality

<p>3D modelling of archaeological and historical structures is the new frontier in the field of conservation science. Similarly, the identification of buried finds, which enhances their multimedia diffusion and restoration, has gained relevance. As such sites often have a high level of structural complexity and complicated territorial geometries, accuracy in the creation of 3D models and the use of sophisticated algorithms for georadar data analysis are crucial. This research is the first step in a larger project aimed at reclaiming the ancient villages located in the Greek area of southern Italy. The present study focuses on the restoration of the village of Africo (RC), a village hit by past flooding. The survey began with a laser scan of the church of St. Nicholas, using both the Faro Focus3D and the Riegl LMS-Z420i laser scanner. At the same time, georadar analyses were carried out in order to pinpoint any buried objects. In the processing phase, our own MATLAB algorithms were used for both laser scanner and georadar datasets and the results compared with those obtained from the scanners’ respective proprietary software. We are working to develop a tourism app in both augmented and virtual reality environments, in order to disseminate and improve access to cultural heritage. The app allows users to see the 3D model and simultaneously access information on the site integrated from a variety of repositories. The aim is to create an immersive visit, in this case, to the church of St. Nicholas.</p><p><strong>Highlights:</strong></p><ul><li><p>Use of different algorithms for registration of terrestrial laser scans and analysis of the data obtained.</p></li><li><p>3D acquisition, processing and restitution methodology from georadar data.</p></li><li><p>Implementation of a tourist app in both virtual and augmented reality by integrating geomatics methodologies.</p></li></ul>

PLANE-BASED REGISTRATION OF SEVERAL THOUSAND LASER SCANS ON STANDARD HARDWARE

Abstract. The automatic registration of terrestrial laser scans appears to be a solved problem in science as well as in practice. However, this assumption is questionable especially in the context of large projects where an object of interest is described by several thousand scans. A critical issue inherently linked to this task is memory management especially if cloud-based registration approaches such as the ICP are being deployed. In order to process even thousands of scans on standard hardware a plane-based registration approach is applied. As a first step planar features are detected within the unregistered scans. This step drastically reduces the amount of data that has to be handled by the hardware. After determination of corresponding planar features a pairwise registration procedure is initiated based on a graph that represents topological relations among all scans. For every feature individual stochastic characteristics are computed that are consequently carried through the algorithm. Finally, a block adjustment is carried out that minimises the residuals between redundantly captured areas. The algorithm is demonstrated on a practical survey campaign featuring a historic town hall. In total, 4853 scans were registered on a standard PC with four processors (3.07 GHz) and 12 GB of RAM.

Point cloud registration from local feature correspondences-Evaluation on challenging datasets.

Registration of laser scans, or point clouds in general, is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. A coarse alignment of the point clouds is generally needed before applying local methods such as the Iterative Closest Point (ICP) algorithm. We propose a feature-based approach to point cloud registration and evaluate the proposed method and its individual components on challenging real-world datasets. For a moderate overlap between the laser scans, the method provides a superior registration accuracy compared to state-of-the-art methods including Generalized ICP, 3D Normal-Distribution Transform, Fast Point-Feature Histograms, and 4-Points Congruent Sets. Compared to the surface normals, the points as the underlying features yield higher performance in both keypoint detection and establishing local reference frames. Moreover, sign disambiguation of the basis vectors proves to be an important aspect in creating repeatable local reference frames. A novel method for sign disambiguation is proposed which yields highly repeatable reference frames.

Globally consistent registration of terrestrial laser scans via graph optimization

In this paper we present a framework for the automatic registration of multiple terrestrial laser scans. The proposed method can handle arbitrary point clouds with reasonable pairwise overlap, without knowledge about their initial orientation and without the need for artificial markers or other specific objects. The framework is divided into a coarse and a fine registration part, which each start with pairwise registration and then enforce consistent global alignment across all scans. While we put forward a complete, functional registration system, the novel contribution of the paper lies in the coarse global alignment step. Merging multiple scans into a consistent network creates loops along which the relative transformations must add up. We pose the task of finding a global alignment as picking the best candidates from a set of putative pairwise registrations, such that they satisfy the loop constraints. This yields a discrete optimization problem that can be solved efficiently with modern combinatorial methods. Having found a coarse global alignment in this way, the framework proceeds by pairwise refinement with standard ICP, followed by global refinement to evenly spread the residual errors.The framework was tested on six challenging, real-world datasets. The discrete global alignment step effectively detects, removes and corrects failures of the pairwise registration procedure, finally producing a globally consistent coarse scan network which can be used as initial guess for the highly non-convex refinement. Our overall system reaches success rates close to 100% at acceptable runtimes <1h, even in challenging conditions such as scanning in the forest.

Registration of non-uniform density 3D laser scans for mapping with micro aerial vehicles

Micro aerial vehicles (MAVs) pose specific constraints on onboard sensing, mainly limited payload and limited processing power. For accurate 3D mapping even in GPS-denied environments, we have designed a lightweight 3D laser scanner specifically for the application on MAVs. Similar to other custom-built 3D laser scanners composed of a rotating 2D laser range finder, it exhibits different point densities within and between individual scan lines. When rotated fast, such non-uniform point densities influence neighborhood searches which in turn may negatively affect local feature estimation and scan registration. We present a complete pipeline for 3D mapping including pair-wise registration and global alignment of such non-uniform density 3D point clouds acquired in-flight. For registration, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. For global alignment, we use a graph-based approach making use of the same error metric and iteratively refine the complete vehicle trajectory. In experiments, we show that our approach can compensate for the effects caused by different point densities up to very low angular resolutions and that we can build accurate and consistent 3D maps in-flight with a micro aerial vehicle.

Geometric features for robust registration of point clouds

Several feature detectors for 3D point data have been proposed in the literature. They have been applied to various problems in computer vision and robotics. We use them to solve two fundamental problems in real-time robotics, namely the registration of laser scans as well as the detection of loops and places. We extend and modify existing feature detectors, combine them in a smart way and create a system, that solves these problems efficiently and better that existing other solutions. We evaluate our system with data sets provided by other groups as well as our own data and we compare our results to those obtained with other algorithms.

Fast Registration of Three-Dimensional Laser Scans without Initial Guess

State of the art graph-based 3D simultaneous localization and mapping (SLAM) systems are typically divided into the so-called front- and back end. The front end&#x2019;s task is to align laser scans locally, constructing a graph of the measurements in the process. The back end&#x2019;s task is to optimize this graph in order to find a maximally consistent configuration of the nodes. The well-known iterative closest point (ICP) algorithm is often used to align pairs of laser scans. As it only provides local convergence, it generally requires an approximate guess in order to determine the correct relative transformation. In practice, this guess is often obtained from a robot&#x2019;s odometry. However, in some applications this odometry information is not available and a fast initial guess is needed for real-time operation. Existing feature-based approaches are often unsuitable for real-time operation due to their computational requirements. The authors present a featureless algorithm that is able to compute an approximate transformation between two laser scans quickly, serving as initial guess for ICP. This algorithm is an adaptation to 3D of a 2D correlative scan-matching algorithm by Olson and will be evaluated in the context of 3D SLAM. Comparisons are made to results that use features as they are used in image processing to match images. The authors make some plausible and moderate assumptions on the scan acquisition and experimentally show that these are usually met. The experiments show that the presented algorithm is able to align two 3D laser scans quickly and reliably in the context of mobile robotics and stand-alone laser scanning.

Keypoint-based 4-Points Congruent Sets – Automated marker-less registration of laser scans

We propose a method to automatically register two point clouds acquired with a terrestrial laser scanner without placing any markers in the scene. What makes this task challenging are the strongly varying point densities caused by the line-of-sight measurement principle, and the huge amount of data. The first property leads to low point densities in potential overlap areas with scans taken from different viewpoints while the latter calls for highly efficient methods in terms of runtime and memory requirements.A crucial yet largely unsolved step is the initial coarse alignment of two scans without any simplifying assumptions, that is, point clouds are given in arbitrary local coordinates and no knowledge about their relative orientation is available. Once coarse alignment has been solved, scans can easily be fine-registered with standard methods like least-squares surface or Iterative Closest Point matching. In order to drastically thin out the original point clouds while retaining characteristic features, we resort to extracting 3D keypoints. Such clouds of keypoints, which can be viewed as a sparse but nevertheless discriminative representation of the original scans, are then used as input to a very efficient matching method originally developed in computer graphics, called 4-Points Congruent Sets (4PCS) algorithm. We adapt the 4PCS matching approach to better suit the characteristics of laser scans.The resulting Keypoint-based 4-Points Congruent Sets (K-4PCS) method is extensively evaluated on challenging indoor and outdoor scans. Beyond the evaluation on real terrestrial laser scans, we also perform experiments with simulated indoor scenes, paying particular attention to the sensitivity of the approach with respect to highly symmetric scenes.

Fast registration of laser scans with 4-point congruent sets - what works and what doesn't

Abstract. Sampling-based algorithms in the mould of RANSAC have emerged as one of the most successful methods for the fully automated registration of point clouds acquired by terrestrial laser scanning (TLS). Sampling methods in conjunction with 3D keypoint extraction, have shown promising results, e.g. the recent K-4PCS (Theiler et al., 2013). However, they still exhibit certain improbable failures, and are computationally expensive and slow if the overlap between scans is low. Here, we examine several variations of the basic K-4PCS framework that have the potential to improve its runtime and robustness. Since the method is inherently parallelizable, straight-forward multi-threading already brings down runtimes to a practically acceptable level (seconds to minutes). At a conceptual level, replacing the RANSAC error function with the more principled MSAC function (Torr and Zisserman, 2000) and introducing a minimum-distance prior to counter the near-field bias reduce failure rates by a factor of up to 4. On the other hand, replacing the repeated evaluation of the RANSAC error function with a voting scheme over the transformation parameters proved not to be generally applicable for the scan registration problem. All these possible extensions are tested experimentally on multiple challenging outdoor and indoor scenarios.

RANSAC approach for automated registration of terrestrial laser scans using linear features

Abstract. The registration process of terrestrial laser scans (TLS) targets the problem of how to combine several laser scans in order to attain better information about features than what could be obtained through single scan. The main goal of the registration process is to estimate the parameters which determine geometrical variation between the origins of datasets collected from different locations. Scale, shifts, and rotation parameters are usually used to describe such variation. This paper presents a framework for the registration of overlapping terrestrial laser scans by establishing an automatic matching strategy that uses 3D linear features. More specifically, invariant separation characteristics between 3D linear features extracted from laser scans will be used to establish hypothesized conjugate linear features between the laser scans. These candidate matches are then used to geo-reference scans relative to a common reference frame. The registration workflow simulates the well-known RANndom Sample Consensus method (RANSAC) for determining the registration parameters, whereas the iterative closest projected point (ICPP) is utilized to determine the most probable solution of the transformation parameters from several solutions. The experimental results prove that the proposed methodology can be used for the automatic registration of terrestrial laser scans using linear features.

Complementing TLS Point Clouds by Dense Image Matching

The complementation of terrestrial laser scanning (TLS) by photogrammetric acquisition techniques enables the exploitation of two different measurement principles. TLS with distance measurements can be used for acquiring large-scale point clouds at medium range distances, while image-based surface reconstruction methods enable flexible acquisition with high precision at short distances. Within this paper, an automatic procedure for the combination of photogrammetry and laser scanning is presented. Synthetic images derived from the laser scanning point clouds are used together with camera images in a common Structure-from-Motion (SfM) process. This enables an automatic registration of multiple laser scans. Furthermore, surface information can also be derived from the imagery using dense image matching methods. While the laser scanning data provides the missing scale information, the imagery can complement the dataset to fill gaps, occlusions or to resolve small details. Consequently, large datasets can be connected by registering imagery and laser scans.

Photogrammetric features for the registration of terrestrial laser scans with minimum overlap

Photogrammetric features for the registration of terrestrial laser scans with minimum overlap

Plane-based registration of construction laser scans with 3D/4D building models

With the development of building information modelling (BIM) and terrestrial laser scanning (TLS) in the architecture, engineering, construction and facility management (AEC/FM) industry, the registration of site laser scans and project 3D (BIM) models in a common coordinate system is becoming critical to effective project control. The co-registration of 3D datasets is normally performed in two steps: coarse registration followed by fine registration. Focusing on the coarse registration, model-scan registration has been well investigated in the past, but it is shown in this article that the context of the AEC/FM industry presents specific (1) constraints that make fully-automated registration very complex and often ill-posed, and (2) advantages that can be leveraged to develop simpler yet effective registration methods. This paper thus presents a novel semi-automated plane-based registration system for coarse registration of laser scanned 3D point clouds with project 3D models in the context of the AEC/FM industry. The system is based on the extraction of planes from the laser scanned point cloud and project 3D/4D model. Planes are automatically extracted from the 3D/4D model. For the point cloud data, two methods are investigated. The first one is fully automated, and the second is a semi-automated but effective one-click RANSAC-supported extraction method. In both cases, planes are then manually but intuitively matched by the user. Experiments, which compare the proposed system to software packages commonly used in the AEC/FM industry, demonstrate that at least as good registration quality can be achieved by the proposed system, in a simpler and faster way. It is concluded that, in the AEC/FM context, the proposed plane-based registration system is a compelling alternative to standard point-based registration techniques.

Featured-Based Registration of Terrestrial Laser Scans with Minimum Overlap Using Photogrammetric Data

Currently, there is a considerable interest in 3D object reconstruction using terrestrial laser scanner (TLS) systems due to their ability to automatically generate a considerable amount of points in a very short time. To fully map an object, multiple scans are captured. The different scans need to be registered with the help of the point cloud in the overlap regions. To guarantee reliable registration, the scans should have large overlap ratio with good geometry for the estimation of the transformation parameters among these scans. The objective of this paper is to propose a registration method that relaxes/eliminates the overlap requirement through the utilization of photogrammetrically reconstructed features. More specifically, a point-based procedure, which utilizes nonconjugate points along corresponding linear features from photogrammetric and TLS data, will be used for the registration. The non-correspondence of the selected points along the linear features is compensated for by artificially modifying their weight matrices. The paper presents experimental results from simulated and real datasets to illustrate the feasibility of the proposed procedure.

Automatic registration of large range datasets with spin-images

Abstract Terrestrial laser scanning technique has represented one of the more advances occurred in the last years in the field of data acquisition. Time-of-Flight (TOF) systems provide a fast and reliable tool to measure millions of 3D points allowing a very effective and dense measurement of the surface geometry. Nowadays, the generation of high quality 3D models is a practice applied to different kind of objects: small or medium size artworks, parts of human body, cars, buildings, civil infrastructures (like dams, bridges, plants, etc.) and whole archaeological sites as well. In most cases, in order to capture the whole object geometry a number of single scans need to be acquired from different positions and then stitched together (i.e. registered each other) to generate the full 3D model. The automatization of the registration of multiple scans acquired from a terrestrial laser scanner (TLS) still represents a very attractive research field. The chance to automatically align several point clouds would reduce processing costs in terms of time and human resources. In addition it would allow even non-specialist users to produce 3D models with good quality. This paper contributes to this research area by presenting a method for the automatic registration of very dense point clouds acquired by TLS systems. The proposed solution is an extension to large datasets of an automatic range data registration procedure we developed a few years ago for the modelling of point clouds acquired with close-range laser scanners. Such procedure, based on the spin-images (SIs) algorithm, has been then improved with the introduction of a multi-resolution method that generates a pyramid of spin-images in order to speed up the matching between adjacent scans. The results we present show that this method can be successfully applied for the automatic registration of high density laser scans of complex and large structures of Cultural Heritage.

Skyline-based registration of 3D laser scans

Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-free registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are presented.

Genetic Algorithms for Automatic Registration of Laser Scans with Imperfect and Subdivided Features (GAReg-ISF)

Genetic Algorithms for Automatic Registration of Laser Scans with Imperfect and Subdivided Features (GAReg-ISF)