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Real-time omnidirectional and panoramic stereo

By Joshua Gluckman, Shree K. Nayar and Keith J. Thoresz

Abstract

Traditional stereo systems have a small field of view which limits their usefulness for certain applications. By imaging curved mirrors, the field of view of conventional cameras can be enhanced. In this paper, we present the design of a compact panoramic stereo camera which uses parabolic mirrors and is capable of producing ¡£¢¥¤§¦ panoramic depth maps at the rate of several frames per second. Video surveillance, autonomous navigation, and site modeling are some of the applications which will benefit from such a sensor. 1 Motivation and Background Work Automated video surveillance systems need the ability to detect and track movements over a large 3-D space. Similarly, autonomous navigation systems must perceive and avoid obstacles which may not be in the field of view of conventional cameras. Site modeling, the recovery of 3-D structure of a large scene, is another application which typically requires range data over a large field of view. In these applications, multiple cameras, moving parts or active sensors are often used to compensate for the small field of view of traditional stereo systems. We have designed a stereo system which uses a combination of lenses and curved mirrors (known as a catadioptric system) to capture a wide field of view without moving parts. Previously, several researchers have used panoramic images to compute depth maps. Ishiguro et al. [1992] used panoramic images obtained from a rotating camera to compute depth. Similar rotating systems have been proposed by [Murray, 1995], [McMillan and Bishop, 1995], [Benosman et al., 1996] and [Kang and Szeliski, 1997]. The disadvantage of such systems is that they require mechanical scanning and are thus not practical for real-time applications such as surveillance and navigation. Panoramic images can be acquired in real-time by imaging curved, mirrored surfaces. One of the first uses of ¨ This work was supported in part by DARPA’s VSAM Image Understandin

Publisher: Morgan Kaufmann
Year: 1998
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.9612
Provided by: CiteSeerX
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