Carnegie Mellon Robotics Institute
|Two approaches to improving the accuracy of camera motion estimation from image sequences are the use of omnidirectional cameras, which combine a conventional camera with a convex mirror that magnifies the field of view, and the use of both image and inertial measurements, which are highly complementary. In this paper, we describe optimal batch algorithms for estimating motion and scene structure from either conventional or omnidirectional images, with or without inertial data.
We also present a method for motion estimation from inertial data and the tangential components of image projections. Tangential components are identical across a wide range of conventional and omnidirectional projection models, so the resulting method does not require any accurate projection model. Because this method discards half of the projection data (i.e., the radial components) and can operate with a projection model that may grossly mismodel the actual camera behavior, we call the method ?eckless?motion estimation, but we show that the camera positions and scene structure estimated using this method can be quite accurate.
Associated Center(s) / Consortia:
Field Robotics Center
Number of pages: 6
|Dennis Strelow and Sanjiv Singh, "Reckless motion estimation from omnidirectional image and inertial measurements," IEEE Workshop on Omnidirectional Vision and Camera Networks (OMNIVIS 2003), June, 2003.|
author = "Dennis Strelow and Sanjiv Singh",
title = "Reckless motion estimation from omnidirectional image and inertial measurements",
booktitle = "IEEE Workshop on Omnidirectional Vision and Camera Networks (OMNIVIS 2003)",
month = "June",
year = "2003",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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