FUSION OF AIRBORNE AND TERRESTRIAL IMAGE-BASED 3D MODELLING FOR
ROAD INFRASTRUCTURE MANAGEMENT - VISION AND FIRST EXPERIMENTS
S. Nebiker*, S. Cavegn', H. Eugster*°, K. Laemmer', J. Markram', R. Wagner“
“Institute of Geomatics Engineering, FHNW University of Applied Sciences and Arts Northwestern Switzerland,
Muttenz, Switzerland — (stephan.nebiker, stefan.cavegn, hannes.eugster)@fhnw.ch
? iNovitas AG, Mobile Mapping Solutions, Muttenz, Switzerland — hannes.eugster@inovitas.ch
* Hexagon Geosystems, Geospatial Solutions, Heerbrugg, Switzerland —
(kai.laemmer, jacques.markram, ruediger.wagner)@leica-geosystems.com
Commission IV, WG IV/2
KEY WORDS: Stereoscopic, Image, Matching, Point Cloud, Three-dimensional, Modelling, Visualization, Virtual Reality
ABSTRACT:
In this paper we present the vision and proof of concept of a seamless image-based 3d modelling approach fusing airborne and
mobile terrestrial imagery. The proposed fusion relies on dense stereo matching for extracting 3d point clouds which — in
combination with the original airborne and terrestrial stereo imagery — create a rich 3d geoinformation and 3d measuring space. For
the seamless exploitation of this space we propose using a new virtual globe technology integrating the airborne and terrestrial
stereoscopic imagery with the derived 3d point clouds. The concept is applied to road and road infrastructure management and
evaluated in a highway mapping project combining stereovision based mobile mapping with high-resolution multispectral airborne
road corridor mapping using the new Leica RCD30 sensor.
1. INTRODUCTION
1.1 Background and motivation
Modern road infrastructure management depends on accurate,
reliable and up-to-date geoinformation which is increasingly
gathered using mobile sensors and platforms. First experiments
in video and image based road navigation and infrastructure
management date back over 30 years (Lippman, 1980). And
first experimental mobile mapping vehicles relying on stereo
imagery were developed some 20 years ago (Novak, 1993;
Schwarz et al., 1993). However, over the last decade mobile
LiDAR became the predominant 3d mobile mapping
technology. Despite its benefits, LiDAR data remains difficult
to handle and to interpret by non-geospatial professionals such
as domain experts in road planning and management. They
often prefer imagery over point clouds or ask for co-registered
imagery complementing the LiDAR data. Over the last few
years image-based 3d mobile mapping has been experiencing a
revival. This is mainly due to some dramatic progress in
imaging sensors, onboard data storage and imaging algorithms —
namely dense stereo and multi-image matching — as well as in
distributed and parallel computing technologies such as High-
Performance Computing (HPC) and Cloud Computing. All
these developments enable new and very powerful image-based
stereovision mobile mapping solutions. In parallel to these
trends in mobile mapping we see the emergence of medium-
format airborne imaging sensors capable of capturing very high
resolution multispectral imagery at high data rates. They permit
photogrammetric flights with highly overlapping imaging
patterns which again favour dense image matching algorithms.
One of the first examples of such a new sensor is the Leica
RCD30. It provides 60 MP imagery in RGB and NIR, FMC and
high data capture rates making it an ideal sensor for road
corridor mapping. Last but not least, we also observe progress
in the (web-based) exploitation of airborne and terrestrial
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imagery. Google and Microsoft, for example, recently integrated
(monoscopic) oblique airborne and terrestrial geospatial
imagery, including vehicle-based panoramic imagery, into their
map portals. Furthermore, with the emergence of web-based 3d
graphics standards such as WebGL, they have started to employ
image warping to support dynamic transitions between airborne
and terrestrial imagery. However, these solutions currently
provide neither real (stereoscopic) 3d visualisation nor accurate
3d measurements.
1.2 Road infrastructure management: characteristics and
requirements
Road infrastructure management encompasses a wide spectrum
of tasks and activities which are increasingly supported by 3d
geodata and 3d geoinformation systems. With the introduction
of accurate and highly automated 3d mobile mapping techno-
logies, detailed 3d digitisations of the road environment are
becoming available. These high fidelity digital representations
of the road environment have triggered an actual paradigm shift
in which a large part of the inspection and measurement tasks
no longer have to be carried out in the field but can be
performed at the desk of the different domain experts. This
leads to a significant increase in productivity and to a
significant reduction of safety hazards and traffic obstructions.
Typical road management tasks which could be supported by
dense mobile mapping data range from visual inspection, simple
measurements (e.g. distances or height differences), assessment
of road surface irregularities, road profile extraction, road sign
management to noise mitigation planning or road verge / nature
strip management. The focal points of these diverse tasks vary
accordingly. They include the actual road surface, road signs
and gantries, safety barriers, nonbuilding structures such as
bridges or tunnels, embankments, drainage as well as low- and
high-growing vegetation (see Figure 1). Depending on the task,
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