6D SLAM with Cached kd-tree Search

6D SLAM (Simultaneous Localization and Mapping) or 6D Concurrent Localization and Mapping of mobile robots considers six degrees of freedom for the robot pose, namely, the x, y and z coordinates and the roll, yaw and pitch angles. In previous work we presented our scan matching based 6D SLAM approach, where scan matching is based on the well known iterative closest point (ICP) algorithm [Besl 1992]. Efficient implementations of this algorithm are a result of a fast computation of closest points. The usual approach, i.e., using kd-trees is extended in this paper. We describe a novel search stategy, that leads to significant speed-ups. Our mapping system is real-time capable, i.e., 3D maps are computed using the resources of the used Kurt3D robotic hardware

Multi-resolution ICP for the efficient registration of point clouds based on octrees

In this paper we propose a multiresolution scheme based on hierarchical octrees for the registration of point clouds acquired by lidar scanners. The point density of these point clouds is generally sparse and inhomogeneous, a property that can yield a risk for correct alignment. Experiments demonstrate that our multiresolution technique is a lot faster than the traditional iterative closest point (ICP) algorithm while it is more robust, e.g. in case of abrupt movements of the sensor. We can report a speed-up factor of more than 30, without jeopardizing the level of accuracy. In scenarios for which the level of detail is less critical, e.g. in case of navigation for autonomous robots, we can even achieve a larger speed-up by trading speed for quality

Semantische dreidimensionale Karten für autonome mobile Roboter

Intelligentes autonomes Roboterhandeln in Alltagsumgebungen erfordert den Einsatz von 3D-Karten, in denen Objekte klassifiziert sind. 3D-Karten sind u.a. zur Steuerung notwendig, damit der Roboter komplexen Hindernissen ausweichen und sich mit 6 Freiheitsgraden (x-, y-, z-Position, Nick-, Gier-, und Rollwinkel) lokalisieren kann. Soll der Roboter mit seiner Umgebung interagieren, wird Interpretation unumgänglich. Über erkannte Objekte kann der Roboter Schlussfolgerungen ziehen, sein Wissen wird inspizier- und kommunizierbar. Aus diesen Gründen ist die automatische und schnelle semantische 3D-Modellierung der Umgebung eine wichtige Fragestellung in der Robotik. 3D-Laserscanner sind eine junge Technologie, die die Erfassung räumlicher Daten revolutioniert und Robotern das dreidimensionale Abtasten von Objekten möglich macht. Die vorliegende Arbeit untersucht und evaluiert mit Hilfe eines 3D-Laserscanners und des mobilen Roboters Kurt3D die zur automatischen semantischen 3D-Kartenerstellung notwendigen Algorithmen. Der erste Teil der Arbeit beschäftigt sich mit der Aufgabe, 3D-Scans in einem globalen Koordinatensystem zu registrieren. Korrekte, global konsistente Modelle entstehen durch einen 6D-SLAM Algorithmus. Hierbei werden 6 Freiheitsgrade in der Roboterpose berücksichtigt, geschlossene Kreise erkannt und der globale Fehler minimiert. Die Basis des 6D-SLAM ist ein sehr schneller ICP-Algorithmus. Im zweiten Teil geht es darum, die Punktmodelle mit Semantik zu versehen. Dazu werden 3D-Flächen in einer digitalisierten 3D-Szene detektiert und interpretiert. Anschließend sucht ein effizienter Algorithmus nach Objekten und bestimmt deren Pose, ebenfalls mit 6 Freiheitsgraden. Schließlich wird der in den zahlreichen Experimenten verwendete, mobile Roboter Kurt3D vorgestellt.Semantic three dimensional maps for autonomous mobile robots Intelligent autonomous acting in unstructured environments requires 3D maps with labelled 3D objects. 3D maps are necessary to avoid collisions with complex obstacles and to self localize in six degrees of freedom (x-, y-, z-position, roll, yaw and pitch angle). Meaning becomes inevitable, if the robot has to interact with its environment. The robot is then able to reason about the objects; its knowledge becomes inspectable and communicable. These arguments lead to requiring automatic and fast semantic environment modelling in robotics. A revolutionary method for gaging environments are 3D scanners, which enable robots to scan objects in a non-contact way in three dimensions. The presented work examines and evaluates the algorithms needed for automatic semantic 3D map building using a 3D laser range finder and the mobile robot Kurt3D. The first part deals with the task to register 3D scans in a common coordinate system. Correct, globally consistent models result from a 6D SLAM algorithm. Hereby 6 degrees of freedom of the robot pose are considered, closed-loops are detected and the global error is minimized. 6D SLAM is based on a very fast ICP algorithm. In the second part semantic descriptions are derived from the point model. For that purpose 3D planes are detected and interpreted in the digitalized 3D scene. After that an efficient algorithm detects objects and estimates their pose with 6 degrees of freedom, too. Finally, the mobile robot Kurt3D, that was used in numerous experiments is presented

A multi-resolution methodology for archeological survey: the Pompeii forum

The article reports about a multi-resolution approach developed for the 3D modeling of the entire Roman Forum in Pompei, Italy. The archaeological area, approximately 150 x 80 m, contains more than 350 finds spread all over the forum as well as larger mural structures of previous buildings and temples. The interdisciplinary 3D modeling work consists of a multi-scale image- and range based digital documentation method developed to fulfill all the surveying and archaeological needs and exploit all the potentialities of the actual 3D modeling techniques. Data’s resolution spans from few decimeters down to few millimeters. The employed surveying methodologies have pros and cons which will be addressed and discussed. Preliminary results of the integration of different 3D data in seamlessly textured 3D model, are presented

Automated Construction Progress Tracking using 3D Sensing Technologies

Accurate and frequent construction progress tracking provides critical input data for project systems such as cost and schedule control as well as billing. Unfortunately, conventional progress tracking is labor intensive, sometimes subject to negotiation, and often driven by arcane rules. Attempts to improve progress tracking have recently focused mainly on automation, using technologies such as 3D imaging, Global Positioning System (GPS), Ultra Wide Band (UWB) indoor locating, hand-held computers, voice recognition, wireless networks, and other technologies in various combinations. Three dimensional (3D) imaging technologies, such as 3D laser scanners (LADARs) and photogrammetry have shown great potential for saving time and cost for recording project 3D status and thus to support some categories of progress tracking. Although laser scanners in particular and 3D imaging in general are being investigated and used in multiple applications in the construction industry, their full potential has not yet been achieved. The reason may be that commercial software packages are still too complicated and time consuming for processing scanned data. Methods have however been developed for the automated, efficient and effective recognition of project 3D BIM objects in site laser scans. This thesis presents a novel system that combines 3D object recognition technology with schedule information into a combined 4D object based construction progress tracking system. The performance of the system is investigated on a comprehensive field database acquired during the construction of a steel reinforced concrete structure, Engineering V Building at the University of Waterloo. It demonstrates a degree of accuracy that meets or exceeds typical manual performance. However, the earned value tracking is the most commonly used method in the industry. That is why the object based automated progress tracking system is further explored, and combined with earned value theory into an earned value based automated progress tracking system. Nevertheless, both of these systems are focused on permanent structure objects only, not secondary or temporary. In the last part of the thesis, several approaches are proposed for concrete construction secondary and temporary object tracking. It is concluded that accurate tracking of structural building project progress is possible by combining a-priori 4D project models with 3D object recognition using the algorithms developed and presented in this thesis

Pipe and Ductwork Progress Tracking using 3D Sensing Technologies

Automated construction progress tracking is becoming critical to efficient and effective construction management. More and more construction companies are putting aside the old way of tracking progress, which was mainly based on foremen daily reports and visual inspections, and are adopting 3D sensing technologies as a new and modern way of tracking progress. Technologies such as 3D laser scanners (LADARs) are investigated as a means to acquire comprehensive 3D point-cloud data which can then be studied by management to determine the progress of construction. Although being much more accurate and efficient than visual inspections, this new progress tracking approach can be improved by applying object recognition algorithms that enable an automated progress tracking. This new approach has been investigated by other researchers, but only for progress tracking of structural elements. This study focuses on mechanical objects such as pipes and ducts, which would give the progress tracking a better level of detail and a wider scope. The investigation is carried out on a field database acquired during the construction of the Engineering VI Building at the University of Waterloo. It was found that the laser scanning technology is a suitable method for acquiring point-clouds of pipes and ductwork, and also that the object recognition algorithm used in this study allows a progress tracking as well as a quality tracking of the HVAC system installation