ISMAR 2004

3D Modeling of Wide Area Outdoor Environments
by Integrating Omnidirectional Range and Color Images
Toshihiro ASAI, Masayuki KANBARA, and Naokazu YOKOYANara Institute of Science and Technology
8916-5 Takayama, Ikoma, Nara 630-0192, Japan{toshih-a, kanbara, yokoya}@is.naist.jp
RTK-GPS

Abstract
This paper describes a method for modeling wide
area outdoor environments by integrating omnidirectional range and color images. The proposed method
effectively reconstructs the 3D models of outdoor
environments by using omnidirectional laser rangefinderand omnidirectional multi-camera system (OMS). In thispaper, we also give experimental results of 3D wide area
reconstruction using the data acquired at 50 points in ourcampus.

Omnidirectional
multi-camera system

Omnidirectionallaser rangefinder(a) The sensor system

1. Introduction
3D models of outdoor environments can be used in a
number of applications such as navigation and virtual
walkthrough. However, such 3D models are often mademanually with high costs, so recently automatic 3D
modeling has been widely investigated; for example
measuring outdoor environments by a laser rangefinder[1] and 3D shape estimation from an image sequence [2].
This paper proposes a 3D reconstruction method for wide
area outdoor environments. By using an omnidirectionallaser rangefinder and an omnidirectional multi-camera
system (OMS) which can capture a wide-angle highresolution image, the 3D outdoor environment model iseffectively generated.

(b) An omnidirectional range image

2. 3D Modeling of Outdoor Environments(c) An omnidirectional color image

2.1. Data Acquisition

Fig.1 Sensors and acquired data.

Fig. 1(a) illustrates the sensor system mounted on avehicle. The system equips an omnidirectional laser
rangefinder (Riegl, LMS-Z360), an OMS (Point GreyResearch, Ladybug), and an RTK-GPS. Geometrical
relationships among these sensor coordinate systems arefixed, therefore the alignment between sensor coordinate
systems are required only once.
Fig. 1(b) shows an acquired omnidirectional range
image in which the distance from the rangefinder is coded
in intensity. Note that the maximum measurable range ofrangefinder is about 200m and its measurement accuracy
is within 12mm. The OMS consists of six cameras inorder to acquire high-resolution omnidirectional colorimages and is calibrated geometrically and photometoric-ally in advance[3]. The RTK-GPS is used to measure theposition of the sensor system and its measurement
accuracy is about 3cm.

2.2. Registration of Range Images
The position of the range data is acquired by the RTKGPS. The orientation of the range data is calculated byapplying the Iterative Closest Point (ICP) algorithm [4] torange data. In the conventional ICP algorithm, thedistance between points in paired range data is defined as
an error, and the rotation matrix is calculated so that theerrors converge. The present rangefinder measures the1

ISMAR 2004

distance by rotating the laser scan, thus the spatial densityof data points depends on the distance; that is, closeobjects are measured densely and far objects aremeasured sparsely. This causes a problem in registering
range data obtained at different positions. In order toovercome this problem, we define an error by computing
the distance between a point in one data set and a plane
determined by adjacent points in the other data set. Wholemodel is generated by sequentially registering a pair ofrange data whose acquired positions adjoin each other.

2.3. Integrating Range and Color Images
The 3D shape obtained in the previous section is
texture-mapped by omnidirectional color images. The
position and orientation of OMS are also simultaneously
estimated by registering range images because geometrical relationship between the rangefinder and the OMS
is fixed and aligned.
Each triangular patch on the 3D shape is colored by
the texture from the image which gives the highest
resolution. However, this strategy fails when an occlusion
occurs. The occlusion is detected when the whole 3D
shape intersects with a triangular pyramid determined bytriangular patch vertexes and the projection center ofcamera. In such a case, the second highest resolution
image is selected.

This paper has proposed a 3D modeling method which
is based on integrating omnidirectional range and color
images for wide area outdoor environments. In experiments, a 3D model is actually generated from omnidirectional range and color images acquired at 50 points in
our campus. Moreover, we can move viewpoint and lookaround the model freely.

3. Experiments

Reference

Fig.2 2D CAD data and a generated model

Examples of rendering the generated model are shown in
Fig. 3.

4. Conclusion

We have carried out experiments of reconstructing ourcampus. In experiments, the range and color images areacquired at 50 points in our campus (about 200m x 300m).
The resolution of each range image is 904 x 450. The
transformation matrix between rangefinder and OMS
coordinates is estimated by pairing corresponding pointsin a range and a color image manually. The antenna of theRTK-GPS is installed in a site measurable from therangefinder.
The number of polygons of the generated 3D model is2,930,462. Fig. 2 illustrates a 2D CAD data of ourcampus with the generated model data. We confirm thatthe generated model has no large distortion.

[1] C. Früh and A. Zakhor: “Constructing 3D city models bymerging aerial and ground views,” IEEE Computer Graphics
and Applications, Vol. 23, pp. 52-61, 2003.
[2] T. Sato, M. Kanbara, N. Yokoya, and H. Takemura: “Dense3-D reconstruction of an outdoor scene by hundreds-baseline
stereo using a hand-held video camera”, International Jour. ofComputer Vision, Vol. 47, No. 1-3, pp. 119-129, 2002.
[3] S. Ikeda, T. Sato, and N. Yokoya: “Panoramic movie
generation using an omnidirectional multi-camera system fortelepresence,” Proc. 13th Scandinavian Conf. on Image Analysis,
pp. 1074-1081, 2003.
[4] P. J. Besl and N. D. Mckay: “A method for registration of3D shapes,” IEEE Trans. on Pattern Analysis and MachineIntelligence, Vol. 14 No. 2, pp. 239-256, 1992.

Fig. 3 Generated 3D model2