Evaluation of the Convergence Region of an Automated Registration Method for 3D Laser Scanner Point Clouds

Using three dimensional point clouds from both simulated and real datasets from close and terrestrial laser scanners, the rotational and translational convergence regions of Geometric Primitive Iterative Closest Points (GP-ICP) are empirically evaluated. The results demonstrate the GP-ICP has a larger rotational convergence region than the existing methods, e.g., the Iterative Closest Point (ICP)

Automated registration of unorganised point clouds from terrestrial laser scanners

Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different locations of a laser scanner must be transformed into a common coordinate system. If good a priori alignment is provided and the point clouds share a large overlapping region, existing registration methods, such as the Iterative Closest Point (ICP) or Chen and Medioni’s method, work well. In practical applications of laser scanners, partially overlapping and unorganised point clouds are provided without good initial alignment. In these cases, the existing registration methods are not appropriate since it becomes very difficult to find the correspondence of the point clouds. A registration method, the Geometric Primitive ICP with the RANSAC (GPICPR), using geometric primitives, neighbourhood search, the positional uncertainty of laser scanners, and an outlier removal procedure is proposed in this thesis. The change of geometric curvature and approximate normal vector of the surface formed by a point and its neighbourhood are used for selecting the possible correspondences of point clouds. In addition, an explicit expression of the position uncertainty of measurement by laser scanners is presented in this dissertation and this position uncertainty is utilised to estimate the precision and accuracy of the estimated relative transformation parameters between point clouds. The GP-ICPR was tested with both simulated data and datasets from close range and terrestrial laser scanners in terms of its precision, accuracy, and convergence region. It was shown that the GP-ICPR improved the precision of the estimated relative transformation parameters as much as a factor of 5.In addition, the rotational convergence region of the GP-ICPR on the order of 10°, which is much larger than the ICP or its variants, provides a window of opportunity to utilise this automated registration method in practical applications such as terrestrial surveying and deformation monitoring

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

Change detection and deformation monitoring is an active area of research within the field of engineering surveying as well as overlapping areas such as structural and civil engineering. The application of Terrestrial Laser Scanning (TLS) techniques for change detection and deformation monitoring of concrete structures has increased over the years as illustrated in the past studies. This paper presents a review of literature on TLS application in the monitoring of structures and discusses registration and georeferencing of TLS point cloud data as a critical issue in the process chain of accurate deformation analysis. Past TLS research work has shown some trends in addressing issues such as accurate registration and georeferencing of the scans and the need of a stable reference frame, TLS error modelling and reduction, point cloud processing techniques for deformation analysis, scanner calibration issues and assessing the potential of TLS in detecting sub-centimetre and millimetre deformations. However, several issues are still open to investigation as far as TLS is concerned in change detection and deformation monitoring studies such as rigorous and efficient workflow methodology of point cloud processing for change detection and deformation analysis, incorporation of measurement geometry in deformation measurements of high-rise structures, design of data acquisition and quality assessment for precise measurements and modelling the environmental effects on the performance of laser scanning. Even though some studies have attempted to address these issues, some gaps exist as information is still limited. Some methods reviewed in the case studies have been applied in landslide monitoring and they seem promising to be applied in engineering surveying to monitor structures. Hence the proposal of a three-stage process model for deformation analysis is presented. Furthermore, with technological advancements new TLS instruments with better accuracy are being developed necessitating more research for precise measurements in the monitoring of structures

3D Modelling from Real Data

The genesis of a 3D model has basically two definitely different paths. Firstly we can consider the CAD generated models, where the shape is defined according to a user drawing action, operating with different mathematical “bricks” like B-Splines, NURBS or subdivision surfaces (mathematical CAD modelling), or directly drawing small polygonal planar facets in space, approximating with them complex free form shapes (polygonal CAD modelling). This approach can be used for both ideal elements (a project, a fantasy shape in the mind of a designer, a 3D cartoon, etc.) or for real objects. In the latter case the object has to be first surveyed in order to generate a drawing coherent with the real stuff. If the surveying process is not only a rough acquisition of simple distances with a substantial amount of manual drawing, a scene can be modelled in 3D by capturing with a digital instrument many points of its geometrical features and connecting them by polygons to produce a 3D result similar to a polygonal CAD model, with the difference that the shape generated is in this case an accurate 3D acquisition of a real object (reality-based polygonal modelling). Considering only device operating on the ground, 3D capturing techniques for the generation of reality-based 3D models may span from passive sensors and image data (Remondino and El-Hakim, 2006), optical active sensors and range data (Blais, 2004; Shan & Toth, 2008; Vosselman and Maas, 2010), classical surveying (e.g. total stations or Global Navigation Satellite System - GNSS), 2D maps (Yin et al., 2009) or an integration of the aforementioned methods (Stumpfel et al., 2003; Guidi et al., 2003; Beraldin, 2004; Stamos et al., 2008; Guidi et al., 2009a; Remondino et al., 2009; Callieri et al., 2011). The choice depends on the required resolution and accuracy, object dimensions, location constraints, instrument’s portability and usability, surface characteristics, working team experience, project’s budget, final goal, etc. Although aware of the potentialities of the image-based approach and its recent developments in automated and dense image matching for non-expert the easy usability and reliability of optical active sensors in acquiring 3D data is generally a good motivation to decline image-based approaches. Moreover the great advantage of active sensors is the fact that they deliver immediately dense and detailed 3D point clouds, whose coordinate are metrically defined. On the other hand image data require some processing and a mathematical formulation to transform the two-dimensional image measurements into metric three-dimensional coordinates. Image-based modelling techniques (mainly photogrammetry and computer vision) are generally preferred in cases of monuments or architectures with regular geometric shapes, low budget projects, good experience of the working team, time or location constraints for the data acquisition and processing. This chapter is intended as an updated review of reality-based 3D modelling in terrestrial applications, with the different categories of 3D sensing devices and the related data processing pipelines

3D registration and integrated segmentation framework for heterogeneous unmanned robotic systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors' measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds' alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments

3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors' measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds' alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market