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Large-Scale 3D Point Cloud Processing Tutorial 2013

Abstract Objectives Audience Speakers Participation Topics Schedule Contact Supporting Materials

Tutorial at the 16th International Conference on Advanced Robotics, Montevideo, Uruguay 2013


  • You can now find the slides from the tutorial presentations here.
  • We uploaded a VM disk image with the software and datasets for the tutorial here.


The increasing need for rapid characterization and quantification of complex environments has created challenges for data analysis. This critical need comes from many important areas, including industrial automation, architecture, agriculture, and the construction or maintenance of tunnels and mines. 3D CAD models are necessary for factory design, facility management, urban and regional planning On one hand, precise 3D data is nowadays reliably obtained by using professional 3D laser scanners. On the other hand, efficient, large-scale 3D point clouds processing is required to process the enormous amount of data. The tutorial will give insights to state of the art acquisition methods and software for addressing these challenges.

Motivation and Objectives

Recently, 3D point cloud processing became popular in the robotics community due to the appearance of the Microsoft kinect camera. The kinect is a structured light laser scanner that obtains a colored 3D point cloud also called RGB-D image, with more than 300000 points at a frame rate of 30Hz. The optimal range of the kinect camera is 1.2 to 3.5 meters and is well suited for indoor robotics in office or kitchen-like environments. Besides the boost of 3D point cloud processing through the kinect, the field of professional 3D laser scanning has advanced.

Light Detection and Ranging (LiDAR) is a technology for three-dimensional measurement of object surfaces. Aerial LiDAR has been used for over a decade to acquire highly reliable and accurate measurements of the earth's surface. In the past few years, terrestrial LiDAR systems were produced by a small number of manufacturers. When paired with classical surveying, terrestrial LiDAR delivers accurately referenced geo-data.

The objective of the tutorial is to present the state of the art 3D scanning technology and recent developments for efficient processing of large scale 3D point clouds. Scenes scanned with LiDARs contain often millions to billions of 3D points. The goal of the tutorial is to give an overview of existing techniques and enable field roboticists to use recent methods and implementations, such as 3DTK - The 3D Toolkit and the Las Vegas Reconstruction Toolkit. We create reference material for the participants for subtopics like 3D point cloud registration and SLAM, calibration, filtering, segmentation, and large scale surface reconstruction.

To achieve the objectives and to gain hands-on experiences on the problems occurring, when trying to process large-scale 3D point clouds, the tutorial consists of presentations, software demonstrations and software trials. To this end, participants have to bring their Linux, MacOS or Windows laptops.

Intended Audience

The tutorial consists of several interleaved theoretical and practical parts. This makes the tutorial well-suited for motivated students at all levels (Bachelor, Master, and PhD students) as well as all roboticists who want to gain hands-on experiences on the problems occurring, when trying to process 3D data. Experts, escpecially roboticists having experiences with the point cloud library, are particularly welcomed as well.

List of Speakers

In alphabetical order:

  • HamidReza Houshiar, Jacobs University Bremen gGmbH, Germany
  • Andreas Nüchter, University of Würzburg, Germany
  • Thomas Wiemann, Osnabrueck University, Germany
  • TBA


To participate at the full-day tutorial please register for the tutorial at the ICAR 2013 conference.

List of Topics

  • Introduction and Overview of Laser Scanning Systems

    A general review of range sensor systems is given in the introduction including triangulation and LiDAR systems. State of the art in terrestrial large volume data acquisition is to use high resolution LiDAR systems. These sensors emit a focused laser beam in a certain direction and determine the distance to an object by measuring the reflected light. By measuring the time difference between the emitted and measured signal, the distance to an object surface can be calculated. Laser range finders are distinguished by the method used to determine the object's distance.

    Pulsed wave systems emit a short laser flash and measure the time until the reflected signal reaches the sensor. By the constant speed of light, the distance is calculated (time-of-flight method). Since the speed of light is as fast as 300000 km per second, a time resolution of just about a few pico seconds is necessary to reach an accuracy of about 10 mm. Depending on the intensity of the laser light, very high maximum ranges (up to 1000 m) can be achieved. Besides pulsed laser scanners, systems using continuous light waves exist. They determine the running time of the laser light by measuring the phase shift between the emitted and detected signal. Since the phase shift is only unambiguous in the interval between 0 and 180 degrees, their maximum range is limited, depending on the wavelength of the laser light. Typical values are about 80 m.

  • Basic Data Structures and Point Cloud Filtering

    In this part of the tutorial we cover the problem of storing the data. Due to large environments, the high amount of data, and the desired precision (millimeter scale), grid based approaches, that are commonly used for planar indoor environments in robotics do not work well. We present different types of range images, an octree with low memory footprint and k-d trees. As the choice of the data structure depends on the algorithms, we identify the algorithmic requirements, and how and which data structures support the tasks.

    Three levels of detail for an efficient octree-based visualization. A corresponding octree cubes drawing is given in the bottom right part.

  • Precise Registration and the SLAM Problem

    After a precise 3D scanner captured its environment the data has to be put into a common coordinate system. Registration aligns this data. Computing precise registrations means solving the SLAM problem. For general 3D point clouds, one has to work with six degrees of freedom. Roughly the approaches can be categorized as follows:

    • Marker-based registration, which requires the placement of artificial markers in the scene.
    • Feature-based registration, which uses corresponding natural features of various types, such as intensity feature points, geometric feature points, feature surfaces, etc.
    • Geometry-based registration, where the geometry of the full point cloud is used without a-priori correspondences. The Iterative Closest Point (ICP) algorithm is the most prominent representative of this category.

    • Automatic scan alignment with 3DTK.

  • Segmentation and Normal Estimation

    Three core components of human perception are grouping, constancy, and contrast effects. Segmentation in robot vision approaches this natural way of observing the world by splitting the point clouds in components with constant attributes, and grouping them together. On range images, a few existing image based segmentation methods can be applied. Pure point cloud segmentation methods rely only on geometry information. As the local geometry of a point cloud is described by surface normals, we presents methods for computing these normals efficiently.

    Normal based segmentation of a 3D point cloud acquired in a room with an open door.

  • Meshing and Polygonal Robot Map Generation

    Three dimensional environment representations play an important role in modern robotic applications. They can be used as maps for localization and obstacle avoidance as well as environment models in robotic simulators or for visualization in HRI contexts, e.g., tele operation in rescue scenarios. For mapping purposes, when building high resolution maps of large environments, the huge amount of data points poses a problem. A common approach to overcome the drawbacks of raw point cloud data is to compute polygonal surface representations of the scanned environments. Polygonal maps are compact, thus memory efficient, and, being continuous surface descriptions, offer a way to handle the discretization problem of point cloud data.

    n the context of mobile robotics, polygonal environment maps offer great potential for applications ranging from usage in simulators, virtual environment modeling for tele operation to robot localization by generating virtual scans via ray tracing. Integrating color information from camera images or point cloud data directly adds an additional modality to the representation and offers the possibility to fuse a compact geometric representation with local color information. However, creating polygonal environment maps based on laser scan data manually is a tedious job, hence we will present methods to automatically compute polygonal maps for robotic applications that are implemented into the Las Vegas Surface Reconstruction Toolkit. We will demonstrate several use-cases for such maps like using them as environment maps in Gazebo or for generation of synthetic point cloud data for localization purposes using ray tracing.

    Example reconstruction using LVR. The input point cloud (left) is automatically transferred into a textured polygonal representation. The number of elements in the stored data was reduced from 12 million colored points to 82.000 textured triangles using 126 bitmaps.

  • Semantic 3D Mapping

    A recent trend in the robotics community is semantic perception, mapping and exploration , which is driven by scanning scenes with RGB-D sensors. A semantic map for a mobile robot is a map that contains, in addition to spatial information about the environment, assignments of mapped features to entities of known classes. Further knowledge about these entities, independent of the map contents, is available for reasoning in some knowledge base with an associated reasoning engine. In this part of the tutorial we explore, how background knowledge gives a boost to model-based object recognition in large-scale 3D laser data.

    Semantically labeled point cloud of an office scene. The detected furniture was recognized using plane detection and background knowledge about the planar relations within the corresponding CAD models.


The Tutorial will take place on Monday, the 25th of November. Each of the parts will contain a lecture followed by software demonstrations and programming sessions.

A detailed schedule will be released soon.

Supporting Materials

Here you can find the slides from the tutorial presentations:

3D Point Cloud Processing - Introduction
3D Point Cloud Processing - Basic Data Structures
3D Point Cloud Processing - Registration
3D Point Cloud Processing - Mobile Mapping
3D Point Cloud Processing - Features
3D Surface Reconstruction - Las Vegas Reconstruction

We have prepared a Virtual Machine Disk file containing all software and data sets needed for the tutorial. You can obtain it as single file or splitted into 4GB chunks for FAT file systems:

Full image (26 GB)

Splitted Image Part 1
Splitted Image Part 2
Splitted Image Part 3
Splitted Image Part 4
Splitted Image Part 5
Splitted Image Part 6
Splitted Image Part 7


Main Organizer

Andreas Nüchter

Informatics VII : Robotics and Telematics
Julius-Maximilians-University Würzburg
Am Hubland
D-97074 Würzburg


Thomas Wiemann

University of Osnabrück
Institute of Computer Science
Albrechtstraße 28
D-49069 Osnabrück