[robotics-worldwide] Tutorial: Data Structures for Large 3D Point Cloud Processing [meetings]

Andreas Nuechter andreas at nuechti.de
Fri Mar 28 02:53:51 PDT 2014

Call for participation

Data Structures for Large 3D Point Cloud Processing

Tutorial at the 13th International Conference on Intelligent
Autonomous Systems


Recently, 3D point cloud processing became popular in the robotics
community due to the appearance of the Microsoft kinect camera and
other low cost sensors. 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 3.5m 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. Typical laser scanners gage
up to 1 million 3D points per second of the surrounding with
millimeter accuracy.

The objective of the tutorial is to present data structures used for
state of the art 3D scanning technology. Efficient data processing is
a key issue of 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 ( http://threedtk.de )
and the Las Vegas Reconstruction Toolkit
( http://www.las-vegas.uni-osnabrueck.de/ ). The focus of this tutorial
lies on point cloud data structures and their implementation in C/C++,
i.e., we discuss, range images, octrees, k-d trees in detail. In
addition, data structures for the marching cubes algorithm and for
meshing methods will play a central role. We create reference material
for the participants for subtopics like 3D point cloud registration
and SLAM, calibration, filtering, segmentation, meshing, 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 to run the provided
virtual machine.


Prof. Dr. Andreas Nüchter
University of Würzburg
Am Hubland, 97074 Würzburg, Germany

Dr. Thomas Wiemann
University of Osnabrück
Albrechtstr. 28, 49069 Osnabrück, Germany

We are looking forward to meeting you at IAS-13.

Very best,
   Andreas Nuechter

Prof. Dr. Andreas Nuechter

Informatics VII : Robotics and Telematics
Informatics building, room B110
Julius-Maximilians-University Wuerzburg
Am Hubland
D-97074 Wuerzburg
+49-931-31-88790                             Wilhelm-Doles-Str. 20
+49-9303-3073297 (homeoffice)                D-97246 Eibelstadt
+49-177-7951270  (mobile)                    Germany
ICQ: 19506497
skype: nuechter76                            +49-9303-3073295
nuechter at informatik.uni-wuerzburg.de         andreas at nuechti.de

Choose a job you love, and you will never have to work a day in your
life. (Confucius)

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