Table Of ContentSystem Design and Real-Time Guidance
of an Unmanned Aerial Vehicle for
Autonomous Exploration
of Outdoor Environments
Dissertation
zur Erlangung des akademischen Grades
Dr. rer. nat.
an der Fakulta¨t fu¨r Mathematik,
Informatik und Naturwissenschaften
der Universita¨t Hamburg
eingereicht beim Fachbereich Informatik von
Benjamin Adler
aus Hamburg
November 2014
Gutachter:
Prof. Dr. Jianwei Zhang
Prof. Dr.-Ing. H. Siegfried Stiehl
Tag der Disputation: 27. Februar 2015
Acknowledgements
Completing this PhD project and writing this document took almost 4 years. During
thistime, thecharacteroftheprojectoftenchanged, allowingmetoworkwithdifferent
people, read literature, create software and integrate hardware. As such, it was a
very interesting and rewarding time, bringing about many failures as well as a few
achievements.
First of all, I would like to thank my advisor Prof. Dr. Jianwei Zhang for being a
constant source of guidance, support and criticism throughout these years. Also, Prof.
Dr.-Ing. H. Siegfried Stiehl kindly accepted to review this thesis, taking a lot of time
and giving helpful guidance in the process. Thank you, Prof. Stiehl!
Furthermore, I would like to express gratitude to my colleagues at TAMS, who pro-
vided a very productive environment in which ideas can evolve from “ridiculous!” to
“works!”. When everyone is keen to listen, laugh and help, this makes for a great place
to learn.
Houxiang, thank you for giving me a great start!
Special thanks go out to you, Bernd, for your generous support, incredible patience
and thoughtful advice. Also, thank you for always having time to hold the fishing
rod!
Junhao, I am grateful for both our friendship and collaboration. Because of you, I
got to see another culture, learn a tiny bit of Chinese history and eat a lot of great
food!
Benjamin Adler
v
Acknowledgements
vi
Abstract
The central endeavor of this thesis is the successful development and evaluation of an
aerial mobile robotic system that produces precise, georeferenced, three-dimensional
maps of outdoor environments by means of autonomous exploration.
The system consists of a ground station and a custom-built UAV with six degrees
of freedom, featuring an on-board computer, an inertial navigation system, and two
2D laser range finders. In addition to a description of the hardware architecture and
individual components being used, this dissertation presents challenges and problems
that arose during the construction of its hard- and software as well as optimizations
applied in the course of its development.
Afundamentalpartofthisworkisthedistributedsoftwarearchitectureforin-flightsen-
sor fusion and data analysis, with a focus on a novel, truly three-dimensional algorithm
generatingmultiplenext-best-views(NBVs). Designedforapplicationonairborneplat-
formsinoutdoorenvironments, theapproachworksdirectlyonraw, unstructuredpoint
clouds and can be used either indoors or outdoors with any sensor generating spatial
occupancy information.
Based on the generated sensor-poses and the incrementally growing point cloud, tra-
jectories are computed for the UAV to autonomously map its environment. To ensure
safe operation, collision avoidance constantly monitors the planned path and updates
it whenever obstacles are detected.
In order to satisfy real-time constraints, all algorithms are implemented on a highly
parallel SIMD architecture found in modern GPUs, allowing for extremely fast motion
planning and responsive visualization. As the underlying hardware imposes limitations
with regards to memory access and concurrency, necessary data structures and further
performance considerations are explained in detail.
Data has been captured during real, autonomous flights and is used to analyze the
performance of all major components (flight controller, next-best-view generation, dy-
namic path planning and collision avoidance) of the system in realistic outdoor sce-
narios. The performance of the GPU-based next-best-view algorithm is also compared
against a previous, CPU-based proof of concept.
vii
Abstract
viii
Kurzfassung
Die vorliegende Dissertation berichtet u¨ber die Entwicklung eines luftgestu¨tzten Robo-
tersystems zur autonomen Erkundung von Außenumgebungen mit dem Ziel der Erstel-
lung pr¨aziser, georeferenzierter Karten.
DasvorgestellteSystembestehtauseinerBodenstationsowieeinereigenskonstruierten
Drohne,welcheeinenComputer,einNavigationsystemundzweiLaserscannermitfu¨hrt.
Neben einer Beschreibung der Hardwarearchitektur und ihrer Komponenten werden
Herausforderungen und Einschr¨ankungen bei Systemintegration und Softwareentwick-
lung, sowie Optimierungen im sp¨ateren Verlauf der Entwicklung pr¨asentiert.
Ein zentraler Teil der Arbeit ist eine verteilte Softwarearchitektur zur Fusion und Ana-
lyse von Sensordaten im Flug, wobei hier der Fokus auf einem neuartigen Algorithmus
zur Erstellung von next-best-views aus bereits erstelltem Kartenmaterial liegt. Obwohl
zur Anwendung auf luftgestu¨tzten Plattformen zur Kartographie von Außenumgebun-
gen mittels LIDAR erdacht, ist der Algorithmus direkt auf jegliche ra¨umliche Sen-
sordaten (z.B. von s¨amtlichen Laserscannern, RGBD- und Time-of-Flight-Kameras)
anwendbar. Basierend auf den erzeugten Sensorpositionen und dem aktuellen Stand
der Karte erzeugt das System sichere Flugrouten zur weiteren Kartierung des Gebiets
und u¨berwacht diese auf Hindernisse.
Um einen Einsatz in Echtzeit zu erm¨oglichen, sind die Algorithmen auf parallel arbei-
tenden GPUs implementiert. Dies erm¨oglicht nicht nur schnelle Flugplanung, sondern
auch effiziente und interaktive Visualisierung der aufgenommenen Daten und der Ar-
beitsweisedereingesetztenAlgorithmen. DadiezugrundeliegendeHardwarebesondere
Anforderungen an Speicherzugriffsmuster und Nebenla¨ufigkeit stellt, werden Daten-
strukturenundweitereU¨berlegungenzurLeistungssteigerungimDetailerla¨utert.
Weiterer Bestandteil der Arbeit ist eine Analyse von Daten aus echten Flu¨gen in
Außenumgebungen zur Beurteilung der Anwendbarkeit und Leistungsf¨ahigkeit aller
verwendeten Komponenten (Flugsteuerung, Erzeugung von next-best-views, Pfadpla-
nungundKollisionsvermeidung). FernerwirddasLaufzeitverhaltendesGPU-basierten
next-best-view Algorithmus mit dem eines CPU-basierten Prototyps verglichen.
ix
Kurzfassung
x
Description:Data has been captured during real, autonomous flights and is used to analyze the .. NAVSTAR Navigation System using Timing and Ranging ToF Time of Flight. TSP Traveling Salesman Problem. UAS Unmanned Aircraft System. UAV Unmanned Aerial Vehicle. UTM Universal Transverse Mercator.
