977 
THE AUTONOMOUS MINI HELICOPTER: 
A POWERFUL PLATFORM FOR MOBILE MAPPING 
Henri Eisenbeiss 
Institute of Geodesy and Photogrammetry, ETH Zurich, CH-8093, Zurich, Switzerland, +41 44 633 32 87 
ehenri@geod.baug.ethz.ch 
Commission I , ICWG I/V 
KEY WORDS: Acquisition, Automation, Integration, Mobile, Mapping, Processing, UAV 
ABSTRACT: 
In this paper, the developments related to an autonomous airborne mobile mapping system for photogrammetric processing will be 
presented. During the last years the author has been involved in several projects related to mobile mapping using an autonomously 
flying model helicopter, a so-called mini UAV (Unmanned Aerial Vehicle). The overall motivation of using mini UAVs for mobile 
mapping, the developed workflow for UAV-data processing, the current status of the work, and recent developments related to 
specific applications are described. In a first step our mini UAV-system and our approach are explained and the need for specific 
developments is highlighted. In the following two applications will be described: The Randa rockslide and the maize project, 
demonstrating our developments for the automation of the photogrammetric workflow using the mini UAV system. In the Randa 
project a unique flight planning tool for UAV-monitoring of mountainous areas was developed. The tool allows for the adaptation of 
the flight in a way, that image data with 2-3 cm resolution of the cliff could be acquired. Moreover, due to this high image resolution, 
a DSM with a higher point density compared to standard airborne and helicopter-based LIDAR-systems, could be generated. In the 
second project the focus was on the automation of the image orientation process. Hence, two test areas (A and B) were defined. 
While in A two commercial photogrammetric workstation were evaluated, in B the data set was processed using GPS/INS data as 
initial values for the image orientation. Therefore, the manual input for the aerial triangulation was reduced to a minimum user 
interaction. Finally, with the oriented image data two DSMs for the analysis of the outcrossing in maize were generated. Because of 
these developments, the tools for the planning and the data acquisition as well as the workflow for the processing of UAV-images 
were automated. However, the complete workflow still requires manual interaction, which will be discussed here in detail including 
a proposal for future developments. 
1. INTRODUCTION 
Throughout the last years, UAVs (Unmanned Aerial Vehicles) 
have become more and more suitable platforms for data 
acquisition in photogrammetry and mobile mapping. “UAVs 
are to be understood as uninhabited and reusable motorized 
aerial vehicles.” (van Blyenburg, 1999). These vehicles are 
remotely controlled, semi-autonomous, autonomous, or have a 
combination of these capabilities. The term UAV is used 
commonly in the computer science, robotics and artificial 
intelligence communities. Supplementary, in the literature also 
synonyms like Remotely Piloted Vehicle (RPV), Remotely 
Operated Aircraft (ROA) and Unmanned Vehicle Systems 
(UVS) can be found. The definition of UAVs encompasses 
fixed and rotary wings UAVs, lighter-than-air UAVs, lethal 
aerial vehicles, aerial decoys, aerial targets, alternatively 
piloted aircrafts and uninhabited combat aerial vehicles. 
Sometimes, cruise missiles are also referred to as UAVs (van 
Blyenburg, 1999). 
However, a model helicopter was selected for our 
investigations. Model helicopters are clearly defined by the 
Unmanned Vehicle Systems (UVS) International Association as 
mini, close short and medium range UAVs depending on their 
size, endurance, range and flying altitude (UVS, 2008). 
In contrast to standard airplanes, model helicopters are able to 
operate closer to the object. In addition, these systems are 
highly flexible in navigation compared to fixed wing UAVs 
(Bendea et al., 2007) and, in contrast to Microdrones (Nebiker et 
al., 2007), more stable against environmental conditions like wind. 
The developments of model helicopters and comparable 
autonomous vehicles are primarily driven by the artificial 
intelligence community (AAAI, 2008) and have been used mainly 
in the past for military applications with increasing use in the 
civilian sector. 
In the past, model helicopters were already used in 
photogrammetric applications (Eisenbeiss, 2004). However at that 
time, the model helicopters were controlled manually via radio 
link. Nowadays, these new technologies allow low cost 
navigation systems to be integrated in model helicopters, enabling 
them to fly autonomously. This kind of autonomous flying model 
helicopter is called mini UAV system (Blyenburg, 1999; 
Eisenbeiss 2004). These mini UAVs are highly manoeuvrable due 
to the possibility of hovering, change of flight direction around 
the center of rotation as well as the capability for turning the 
mounted camera in horizontal and vertical direction. However, 
due to the difficulty of keeping the ideal position and attitude, the 
vibration of the helicopter and the manual planning of image 
acquisition points, model helicopters have not been used 
successfully in the past for measurements, precise modeling and 
mapping of objects (Eisenbeiss, 2004). Latest developments 
integrate GPS/INS (Global Positioning System / Inertial 
Navigation System) together with a stabilization platform for the 
camera. Because of the small size and the low payload, the 
selection of the installed hardware is mostly limited to low cost 
navigation systems with low precision. Nevertheless, the